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       "<style type='text/css'>\n",
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       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from hyperopt import hp\n",
    "from hyperopt import Trials\n",
    "\n",
    "from lightgbm import LGBMClassifier, early_stopping\n",
    "\n",
    "from shaphypetune import BoostRFE, BoostRFA\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
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   "source": [
    "def binary_performances(y_true, y_prob, thresh=0.5, labels=['Positives','Negatives']):\n",
    "    \n",
    "    import seaborn as sns\n",
    "    import matplotlib.pyplot as plt\n",
    "    from sklearn.metrics import confusion_matrix, auc, roc_curve\n",
    "    \n",
    "    shape = y_prob.shape\n",
    "    if len(shape) > 1:\n",
    "        if shape[1] > 2:\n",
    "            raise ValueError('A binary class problem is required')\n",
    "        else:\n",
    "            y_prob = y_prob[:,1]\n",
    "    \n",
    "    plt.figure(figsize=[15,4])\n",
    "    \n",
    "    #1 -- Confusion matrix\n",
    "    cm = confusion_matrix(y_true, (y_prob>thresh).astype(int))\n",
    "    \n",
    "    plt.subplot(131)\n",
    "    ax = sns.heatmap(cm, annot=True, cmap='Blues', cbar=False, \n",
    "                     annot_kws={\"size\": 14}, fmt='g')\n",
    "    cmlabels = ['True Negatives', 'False Positives',\n",
    "               'False Negatives', 'True Positives']\n",
    "    for i,t in enumerate(ax.texts):\n",
    "        t.set_text(t.get_text() + \"\\n\" + cmlabels[i])\n",
    "    plt.title('Confusion Matrix', size=15)\n",
    "    plt.xlabel('Predicted Values', size=13)\n",
    "    plt.ylabel('True Values', size=13)\n",
    "      \n",
    "    #2 -- Distributions of Predicted Probabilities of both classes\n",
    "    plt.subplot(132)\n",
    "    plt.hist(y_prob[y_true==1], density=True, bins=25,\n",
    "             alpha=.5, color='green',  label=labels[0])\n",
    "    plt.hist(y_prob[y_true==0], density=True, bins=25,\n",
    "             alpha=.5, color='red', label=labels[1])\n",
    "    plt.axvline(thresh, color='blue', linestyle='--', label='Boundary')\n",
    "    plt.xlim([0,1])\n",
    "    plt.title('Distributions of Predictions', size=15)\n",
    "    plt.xlabel('Positive Probability (predicted)', size=13)\n",
    "    plt.ylabel('Samples (normalized scale)', size=13)\n",
    "    plt.legend(loc=\"upper right\")\n",
    "    \n",
    "    #3 -- ROC curve with annotated decision point\n",
    "    fp_rates, tp_rates, _ = roc_curve(y_true, y_prob)\n",
    "    roc_auc = auc(fp_rates, tp_rates)\n",
    "    plt.subplot(133)\n",
    "    plt.plot(fp_rates, tp_rates, color='orange',\n",
    "             lw=1, label='ROC curve (area = %0.3f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], lw=1, linestyle='--', color='grey')\n",
    "    tn, fp, fn, tp = [i for i in cm.ravel()]\n",
    "    plt.plot(fp/(fp+tn), tp/(tp+fn), 'bo', markersize=8, label='Decision Point')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate', size=13)\n",
    "    plt.ylabel('True Positive Rate', size=13)\n",
    "    plt.title('ROC Curve', size=15)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.subplots_adjust(wspace=.3)\n",
    "    plt.show()\n",
    "\n",
    "    tn, fp, fn, tp = [i for i in cm.ravel()]\n",
    "    precision = tp / (tp + fp)\n",
    "    recall = tp / (tp + fn)\n",
    "    F1 = 2*(precision * recall) / (precision + recall)\n",
    "    results = {\n",
    "        \"Precision\": precision, \"Recall\": recall,\n",
    "        \"F1 Score\": F1, \"AUC\": roc_auc\n",
    "    }\n",
    "    \n",
    "    prints = [f\"{kpi}: {round(score, 3)}\" for kpi,score in results.items()]\n",
    "    prints = ' | '.join(prints)\n",
    "    print(prints)"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "((20000, 30), (20000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CREATE SYNTHETIC DATA ### \n",
    "\n",
    "n_feat = 30\n",
    "n_informative_feat = 11\n",
    "n_redundant_feat = 6\n",
    "\n",
    "assert n_feat > 0\n",
    "assert n_informative_feat > 0\n",
    "assert n_redundant_feat > 0\n",
    "assert n_feat > (n_informative_feat + n_redundant_feat + 1)\n",
    "\n",
    "X, y = make_classification(n_samples=20_000, n_features=n_feat, \n",
    "                           n_informative=n_informative_feat, n_redundant=n_redundant_feat,\n",
    "                           n_classes=2, weights=[0.9, 0.1], class_sep=0.5, \n",
    "                           random_state=123, shuffle=False)\n",
    "\n",
    "informartive_feat = range(0, n_informative_feat)\n",
    "redundant_feat = range(informartive_feat[-1]+1, informartive_feat[-1] + n_redundant_feat)\n",
    "noise_feat = range(redundant_feat[-1]+1, n_feat)\n",
    "\n",
    "X = pd.DataFrame(\n",
    "    X, columns= \\\n",
    "    [f\"feat_{c}_info\" for c in informartive_feat] + \\\n",
    "    [f\"feat_{c}_combi\" for c in redundant_feat] + \\\n",
    "    [f\"feat_{c}_noise\" for c in noise_feat] \n",
    ")\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
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   "source": [
    "### DEFINE TUNING VARIABLES ###\n",
    "\n",
    "param_dist_hyperopt = {\n",
    "    'class_weight': hp.choice('class_weight', [None, 'balanced']),\n",
    "    'subsample': hp.uniform('gdbt_subsample', 0.5, 1),\n",
    "    'num_leaves': 5 + hp.randint('num_leaves', 60),\n",
    "    'learning_rate': hp.loguniform('learning_rate', np.log(0.001), np.log(0.3)),\n",
    "    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n",
    "    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n",
    "    'feature_fraction': hp.uniform('feature_fraction', 0.6, 1.0)\n",
    "}\n",
    "\n",
    "def F1(y_true, y_hat):\n",
    "    return 'f1', f1_score(y_true, np.round(y_hat)), True\n",
    "\n",
    "rfe_results = {}\n",
    "rfa_results = {}\n",
    "rfe_scores = {}\n",
    "rfa_scores = {}\n",
    "\n",
    "CV = StratifiedKFold(n_splits=8, shuffle=True, random_state=123)\n",
    "lgbm = LGBMClassifier(n_estimators=200, random_state=123, n_jobs=-1)"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 1 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0002 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0003 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0004 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0005 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0006 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0007 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0008 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0009 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0010 ### iterations: 00019 ### eval_score: 0.56233\n",
      "trial: 0011 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0012 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0013 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0014 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0015 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0016 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0017 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0018 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0019 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0020 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0021 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0022 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0023 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0024 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0025 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0026 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0027 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0028 ### iterations: 00067 ### eval_score: 0.77904\n",
      "trial: 0029 ### iterations: 00072 ### eval_score: 0.77169\n",
      "trial: 0030 ### iterations: 00072 ### eval_score: 0.77169\n",
      "\n",
      "--- 2 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0002 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0003 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0004 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0005 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0006 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0007 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0008 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0009 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0010 ### iterations: 00021 ### eval_score: 0.54743\n",
      "trial: 0011 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0012 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0013 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0014 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0015 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0016 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0017 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0018 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0019 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0020 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0021 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0022 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0023 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0024 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0025 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0026 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0027 ### iterations: 00056 ### eval_score: 0.70833\n",
      "trial: 0028 ### iterations: 00040 ### eval_score: 0.72193\n",
      "trial: 0029 ### iterations: 00040 ### eval_score: 0.72193\n",
      "trial: 0030 ### iterations: 00040 ### eval_score: 0.72193\n",
      "\n",
      "--- 3 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00025 ### eval_score: 0.58101\n",
      "trial: 0002 ### iterations: 00025 ### eval_score: 0.58101\n",
      "trial: 0003 ### iterations: 00025 ### eval_score: 0.58101\n",
      "trial: 0004 ### iterations: 00025 ### eval_score: 0.58101\n",
      "trial: 0005 ### iterations: 00025 ### eval_score: 0.58101\n",
      "trial: 0006 ### iterations: 00025 ### eval_score: 0.58101\n",
      "trial: 0007 ### iterations: 00030 ### eval_score: 0.55342\n",
      "trial: 0008 ### iterations: 00030 ### eval_score: 0.55342\n",
      "trial: 0009 ### iterations: 00030 ### eval_score: 0.55342\n",
      "trial: 0010 ### iterations: 00030 ### eval_score: 0.55342\n",
      "trial: 0011 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0012 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0013 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0014 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0015 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0016 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0017 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0018 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0019 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0020 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0021 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0022 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0023 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0024 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0025 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0026 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0027 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0028 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0029 ### iterations: 00044 ### eval_score: 0.75688\n",
      "trial: 0030 ### iterations: 00044 ### eval_score: 0.75688\n",
      "\n",
      "--- 4 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0002 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0003 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0004 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0005 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0006 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0007 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0008 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0009 ### iterations: 00013 ### eval_score: 0.57182\n",
      "trial: 0010 ### iterations: 00013 ### eval_score: 0.57258\n",
      "trial: 0011 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0012 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0013 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0014 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0015 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0016 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0017 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0018 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0019 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0020 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0021 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0022 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0023 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0024 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0025 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0026 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0027 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0028 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0029 ### iterations: 00039 ### eval_score: 0.77904\n",
      "trial: 0030 ### iterations: 00039 ### eval_score: 0.77904\n",
      "\n",
      "--- 5 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0002 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0003 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0004 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0005 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0006 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0007 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0008 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0009 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0010 ### iterations: 00021 ### eval_score: 0.56988\n",
      "trial: 0011 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0012 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0013 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0014 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0015 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0016 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0017 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0018 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0019 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0020 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0021 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0022 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0023 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0024 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0025 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0026 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0027 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0028 ### iterations: 00047 ### eval_score: 0.7366\n",
      "trial: 0029 ### iterations: 00052 ### eval_score: 0.73412\n",
      "trial: 0030 ### iterations: 00052 ### eval_score: 0.73412\n",
      "\n",
      "--- 6 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0002 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0003 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0004 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0005 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0006 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0007 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0008 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0009 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0010 ### iterations: 00015 ### eval_score: 0.58493\n",
      "trial: 0011 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0012 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0013 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0014 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0015 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0016 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0017 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0018 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0019 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0020 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0021 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0022 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0023 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0024 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0025 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0026 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0027 ### iterations: 00053 ### eval_score: 0.70983\n",
      "trial: 0028 ### iterations: 00047 ### eval_score: 0.75047\n",
      "trial: 0029 ### iterations: 00047 ### eval_score: 0.75047\n",
      "trial: 0030 ### iterations: 00047 ### eval_score: 0.75047\n",
      "\n",
      "--- 7 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0002 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0003 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0004 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0005 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0006 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0007 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0008 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0009 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0010 ### iterations: 00018 ### eval_score: 0.57609\n",
      "trial: 0011 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0012 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0013 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0014 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0015 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0016 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0017 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0018 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0019 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0020 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0021 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0022 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0023 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0024 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0025 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0026 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0027 ### iterations: 00058 ### eval_score: 0.73636\n",
      "trial: 0028 ### iterations: 00052 ### eval_score: 0.746\n",
      "trial: 0029 ### iterations: 00052 ### eval_score: 0.746\n",
      "trial: 0030 ### iterations: 00052 ### eval_score: 0.746\n",
      "\n",
      "--- 8 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0002 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0003 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0004 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0005 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0006 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0007 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0008 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0009 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0010 ### iterations: 00021 ### eval_score: 0.58182\n",
      "trial: 0011 ### iterations: 00042 ### eval_score: 0.73636\n",
      "trial: 0012 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0013 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0014 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0015 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0016 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0017 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0018 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0019 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0020 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0021 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0022 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0023 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0024 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0025 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0026 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0027 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0028 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0029 ### iterations: 00045 ### eval_score: 0.72936\n",
      "trial: 0030 ### iterations: 00045 ### eval_score: 0.72936\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### CV Recursive Feature Addition ###\n",
    "\n",
    "y_pred = np.zeros((y.shape[0],2))\n",
    "\n",
    "for i,(id_train,id_test) in enumerate(CV.split(X,y)):\n",
    "    \n",
    "    print(f\"--- {i+1} fold ---\")\n",
    "    \n",
    "    model = BoostRFA(\n",
    "        lgbm, \n",
    "        step=3,\n",
    "        param_grid=param_dist_hyperopt, \n",
    "        greater_is_better=True, \n",
    "        n_iter=30, sampling_seed=123\n",
    "    )\n",
    "    model.fit(\n",
    "        X.iloc[id_train], y[id_train], \n",
    "        trials=Trials(), \n",
    "        eval_set=[(X.iloc[id_test], y[id_test])], \n",
    "        callbacks=[early_stopping(5, verbose=False)], eval_metric=F1\n",
    "    )\n",
    "    y_pred[id_test] = model.predict_proba(X.iloc[id_test])\n",
    "    \n",
    "    rfa_results[f\"{i+1} fold\"] = model.support_\n",
    "    rfa_scores[f\"{i+1} fold\"] = model.best_score_\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13cd89c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T21:24:11.661298Z",
     "iopub.status.busy": "2022-01-10T21:24:11.660567Z",
     "iopub.status.idle": "2022-01-10T21:24:12.363953Z",
     "shell.execute_reply": "2022-01-10T21:24:12.363386Z",
     "shell.execute_reply.started": "2022-01-05T14:02:03.284193Z"
    },
    "papermill": {
     "duration": 0.80651,
     "end_time": "2022-01-10T21:24:12.364102",
     "exception": false,
     "start_time": "2022-01-10T21:24:11.557592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.815 | Recall: 0.694 | F1 Score: 0.75 | AUC: 0.941\n"
     ]
    }
   ],
   "source": [
    "### RFA CV performances ###\n",
    "\n",
    "binary_performances(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7b943e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T21:24:12.568638Z",
     "iopub.status.busy": "2022-01-10T21:24:12.567838Z",
     "iopub.status.idle": "2022-01-10T21:38:39.404252Z",
     "shell.execute_reply": "2022-01-10T21:38:39.405185Z",
     "shell.execute_reply.started": "2022-01-05T13:37:34.646275Z"
    },
    "papermill": {
     "duration": 866.942067,
     "end_time": "2022-01-10T21:38:39.405399",
     "exception": false,
     "start_time": "2022-01-10T21:24:12.463332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- 1 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00021 ### eval_score: 0.56159\n",
      "trial: 0002 ### iterations: 00021 ### eval_score: 0.56159\n",
      "trial: 0003 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0004 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0005 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0006 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0007 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0008 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0009 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0010 ### iterations: 00078 ### eval_score: 0.73239\n",
      "trial: 0011 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0012 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0013 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0014 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0015 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0016 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0017 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0018 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0019 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0020 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0021 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0022 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0023 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0024 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0025 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0026 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0027 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0028 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0029 ### iterations: 00041 ### eval_score: 0.77951\n",
      "trial: 0030 ### iterations: 00041 ### eval_score: 0.77951\n",
      "\n",
      "--- 2 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0002 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0003 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0004 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0005 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0006 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0007 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0008 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0009 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0010 ### iterations: 00021 ### eval_score: 0.54891\n",
      "trial: 0011 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0012 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0013 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0014 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0015 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0016 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0017 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0018 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0019 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0020 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0021 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0022 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0023 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0024 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0025 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0026 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0027 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0028 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0029 ### iterations: 00038 ### eval_score: 0.71818\n",
      "trial: 0030 ### iterations: 00038 ### eval_score: 0.71818\n",
      "\n",
      "--- 3 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00027 ### eval_score: 0.58488\n",
      "trial: 0002 ### iterations: 00027 ### eval_score: 0.58488\n",
      "trial: 0003 ### iterations: 00027 ### eval_score: 0.58488\n",
      "trial: 0004 ### iterations: 00027 ### eval_score: 0.58488\n",
      "trial: 0005 ### iterations: 00027 ### eval_score: 0.58488\n",
      "trial: 0006 ### iterations: 00027 ### eval_score: 0.58488\n",
      "trial: 0007 ### iterations: 00032 ### eval_score: 0.563\n",
      "trial: 0008 ### iterations: 00032 ### eval_score: 0.563\n",
      "trial: 0009 ### iterations: 00032 ### eval_score: 0.563\n",
      "trial: 0010 ### iterations: 00032 ### eval_score: 0.563\n",
      "trial: 0011 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0012 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0013 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0014 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0015 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0016 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0017 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0018 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0019 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0020 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0021 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0022 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0023 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0024 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0025 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0026 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0027 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0028 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0029 ### iterations: 00050 ### eval_score: 0.77064\n",
      "trial: 0030 ### iterations: 00050 ### eval_score: 0.77064\n",
      "\n",
      "--- 4 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00023 ### eval_score: 0.59836\n",
      "trial: 0002 ### iterations: 00023 ### eval_score: 0.59836\n",
      "trial: 0003 ### iterations: 00039 ### eval_score: 0.66334\n",
      "trial: 0004 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0005 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0006 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0007 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0008 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0009 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0010 ### iterations: 00044 ### eval_score: 0.64631\n",
      "trial: 0011 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0012 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0013 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0014 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0015 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0016 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0017 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0018 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0019 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0020 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0021 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0022 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0023 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0024 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0025 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0026 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0027 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0028 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0029 ### iterations: 00051 ### eval_score: 0.79018\n",
      "trial: 0030 ### iterations: 00051 ### eval_score: 0.79018\n",
      "\n",
      "--- 5 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0002 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0003 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0004 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0005 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0006 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0007 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0008 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0009 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0010 ### iterations: 00009 ### eval_score: 0.55601\n",
      "trial: 0011 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0012 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0013 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0014 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0015 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0016 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0017 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0018 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0019 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0020 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0021 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0022 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0023 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0024 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0025 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0026 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0027 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0028 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0029 ### iterations: 00050 ### eval_score: 0.78733\n",
      "trial: 0030 ### iterations: 00050 ### eval_score: 0.78733\n",
      "\n",
      "--- 6 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0002 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0003 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0004 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0005 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0006 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0007 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0008 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0009 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0010 ### iterations: 00025 ### eval_score: 0.58272\n",
      "trial: 0011 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0012 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0013 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0014 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0015 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0016 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0017 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0018 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0019 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0020 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0021 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0022 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0023 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0024 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0025 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0026 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0027 ### iterations: 00067 ### eval_score: 0.74074\n",
      "trial: 0028 ### iterations: 00058 ### eval_score: 0.77014\n",
      "trial: 0029 ### iterations: 00058 ### eval_score: 0.77014\n",
      "trial: 0030 ### iterations: 00058 ### eval_score: 0.77014\n",
      "\n",
      "--- 7 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0002 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0003 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0004 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0005 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0006 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0007 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0008 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0009 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0010 ### iterations: 00016 ### eval_score: 0.57297\n",
      "trial: 0011 ### iterations: 00062 ### eval_score: 0.75113\n",
      "trial: 0012 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0013 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0014 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0015 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0016 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0017 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0018 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0019 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0020 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0021 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0022 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0023 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0024 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0025 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0026 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0027 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0028 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0029 ### iterations: 00060 ### eval_score: 0.73148\n",
      "trial: 0030 ### iterations: 00060 ### eval_score: 0.73148\n",
      "\n",
      "--- 8 fold ---\n",
      "\n",
      "30 trials detected for ('class_weight', 'subsample', 'num_leaves', 'learning_rate', 'reg_alpha', 'reg_lambda', 'feature_fraction')\n",
      "\n",
      "trial: 0001 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0002 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0003 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0004 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0005 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0006 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0007 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0008 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0009 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0010 ### iterations: 00018 ### eval_score: 0.57423\n",
      "trial: 0011 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0012 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0013 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0014 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0015 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0016 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0017 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0018 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0019 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0020 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0021 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0022 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0023 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0024 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0025 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0026 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0027 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0028 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0029 ### iterations: 00052 ### eval_score: 0.75737\n",
      "trial: 0030 ### iterations: 00052 ### eval_score: 0.75737\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### CV Recursive Feature Elimination ###\n",
    "\n",
    "y_pred = np.zeros((y.shape[0],2))\n",
    "\n",
    "for i,(id_train,id_test) in enumerate(CV.split(X,y)):\n",
    "    \n",
    "    print(f\"--- {i+1} fold ---\")\n",
    "    \n",
    "    model = BoostRFE(\n",
    "        lgbm, \n",
    "        step=3,\n",
    "        param_grid=param_dist_hyperopt, \n",
    "        greater_is_better=True, \n",
    "        n_iter=30, sampling_seed=123\n",
    "    )\n",
    "    model.fit(\n",
    "        X.iloc[id_train], y[id_train], \n",
    "        trials=Trials(), \n",
    "        eval_set=[(X.iloc[id_test], y[id_test])], \n",
    "        callbacks=[early_stopping(5, verbose=False)], eval_metric=F1\n",
    "    )\n",
    "    y_pred[id_test] = model.predict_proba(X.iloc[id_test])\n",
    "    \n",
    "    rfe_results[f\"{i+1} fold\"] = model.support_\n",
    "    rfe_scores[f\"{i+1} fold\"] = model.best_score_\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34df8661",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T21:38:39.755357Z",
     "iopub.status.busy": "2022-01-10T21:38:39.754685Z",
     "iopub.status.idle": "2022-01-10T21:38:40.347602Z",
     "shell.execute_reply": "2022-01-10T21:38:40.348120Z"
    },
    "papermill": {
     "duration": 0.770191,
     "end_time": "2022-01-10T21:38:40.348316",
     "exception": false,
     "start_time": "2022-01-10T21:38:39.578125",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ePg9S1e9E5D3gCxFpgnP/xSWcP76Hce59WKqqP4rIH8AcERmBczX2GZyJcG+YyymRFgCPishmYD/OUMG53CuIyGpXva04V3sexLkqdcMIli5TcU54P4jISzhXnkfjXIX+NJnijqSqK3GubsW2/rxraOGXROQCTh/zEThdDtzfa8QE10+IyArggqu7XIK4vtROBuao6jxX2afAJyKyKhW/HJr0z0dEIkbozIkzBPcQnC+87TWW+dpE5CGcFpGlOAMKVcBJVqYDqOo1EfkXZ6qTrThXev9ORHyngBki8gJOIjIWJ9Gb6jpOuIgsBJ5y3Ud7Dmek3uhzO+0ECoszZcpWnAFsDsRwvPdw3v+PIvI/nC7db+Lcw/hNQoMWkdzAjzifx26cC0JP47Qg7YhpG1U9IyLjgBdEJBSnu2F3nImO745pm5SgqufEmTrhA9dV9VU4F7MqAi1VtZur6uvAfHHm41qAM5pf+xh26b7vcHGmfpgpIjNx5gdTnHsNZ7l6RqT5n5UxnqaqKiKv4/wt3a6qP6vqFXGmiFoMrBSR8TgtZHVxRkP/C3jRbR9HXH9ns0SkBM73iojJ2fvgdA2PqxXwXpxu3xtE5H2uT87eDuf7WwPgEM754QFXncU4PUniPFfEYA7OLUBP4QykF5nIqeouEZmIM7fsWzjnTn+gKlBRVQfd5LFMYnl6JBl7KEAPnD/M8zhXUXfjXNkp4lanIM4/vbM4X5h+BepF288B3EbXc5X1J9pIcsQ8UmcOnKkIzuD8M30B5wuc+2icb+P8w76I8+XtF+A2t/VjiDbaIM49Rd+6trmE0wWzQrQ6McVzw75i+NwSUifK6J04o0j9jJOsHsSZ7yzKfnDugXwL58tyOLAyruNx4yhXn+JczcsX7fPdD3zj6d83e6SPB9dHi1XX7+E5nH+Wr7mfG1x1yxB19M5GOP+8j+AkdP/itA66jyTXFifRC3ZtWyb6fqIdI8r5BddojTiJz26cFvTfcRvF1lWvMM4FrQs4E48P5saRHv1xBg854Tr+VPdjRNtfbZwrx1dcn8lXRB3dMsb34L4vnMThM5yWrSuu88T3QPV4fibeOOfFQzjn6u3AvdHq9OfmRu+MtR4xjKrntq4vzpX8IJz/C+uAYdHqDMW5in8F597OtsQxeqdbWXfXvoNxLjIuBkqnp5+VPeyRWg9i/27gjXNu/DFaeTVgLs4I7FdddV4Gssey/9qu+sdxWv6O4LQk1klAbEWAD3C+f1x1nSt+xBlkxr3eSNd57aJr312IefTOG/43uNYLzncqBdrFsv5JnNbPq673/itwn6d/fpnpIa4fhjHGGGOMMcaYDMju6TPGGGOMMcaYDMySPmOMMcYYY4zJwCzpM8YYY4wxxpgMzJI+Y4wxxhhjjMnA0sWUDVlrD7XRZjKQs+s/8nQIJpn5+yCejiGtKVCggJYpU8bTYZhksss1aUulSp6NwySfjRs3nlLVgp6OI62xc5cxaVdSzlvpIukzxpj0pkyZMmzYsMHTYZhk0qKF87xypSejMMnJNXekicbOXcakXUk5b1n3TmOMMcYYY4zJwKylzxhjjInHCy94OgJjjDEm8SzpM8YYY+LRurWnIzDGGGMSz5I+Y4wxJh5btjjPtWql7HFCQkIIDAwkODg4ZQ+Uifj7+1OiRAl8fX09HYoxxniMJX3GGGNMPJ580nlO6YFcAgMDyZkzJ2XKlEHEBsVNKlXl9OnTBAYGUrZsWU+HY4wxHmMDuRhjjDFpRHBwMPnz57eEL5mICPnz57eWU2NMpmdJnzHGGJOGWMKXvDLq5ykik0XkhIhsjWW9iMh4EdkrIn+LSJ3UjtEYk3ZY0meMyRREpKSI/CIi20Vkm4g84SrPJyI/icge13PeWLa/31Vnj4jcn7rRG2PMDaYC7eNY3wGo4HoMBj5JhZiMMWmU3dNnjMksQoGnVXWTiOQENorIT0B/4GdVfVNERgAjgOfcNxSRfMBoIABQ17aLVPVsqr4Dk+mMWTkmeffXIv79eXt7U716dUJDQ6lcuTLTpk0jW7ZsCT7GkSNHePzxx5k3bx5btmzhyJEjdOzYEYBFixaxfft2RowYkdi3YFxUdZWIlImjyp3AdFVVYK2I5BGRoqp6NHUiNMbE6coRCDoM4SEQfg1Cr8ClfSBeznL4NTi/HVTh8n7+O5O0wags6TPGZAquLzpHXa8visgOoDjOF6MWrmrTgJVES/qAdsBPqnoGwJUstgdmxXrAI0dgzJj4A0tIHeNxr7/u6QhST9asWdniGq703nvvZeLEiQwbNizB2xcrVox58+YBsGXLFjZs2BCZ9HXp0oUuXboke8wmRsWBQ27Lga6yG5I+ERmM0xpIqVKlUiU4Y9INVScxU1dyFh4Clw5AWBBc3A2n1jiJmipoGGg4EH79tbpeXzsDVw6Dlw9cPghhVyB7GchaFLz8AFdX9NyVwSuLU+aXH/JUZc2uRqw9dA7nK0riWNJnjMl0XFfHawPrgMJuV76PAYVj2CS2L0/R93v9i1Pu3MkYsfG0xo09HYFn3Hbbbfz999+cOXOGgQMHsn//frJly8akSZOoUaMGv/76K0888QTg3Du3atUqTp8+TadOndi0aRMvvfQSQUFBrF69mpEjRxIUFMSGDRt47bXXqFGjBv/++y9eXl5cvnyZW2+9lf3793Pw4EEeffRRTp48SbZs2fjss8+49dZb+frrrxk7dize3t7kzp2bVatWefjTyThUdRIwCSAgIEA9HI4xSaPhTnIVFgzhV+HCTgg+7mpRcyVvl/bD2b8h9IKTXEUkZpEPt0Qt9DKID3j5utUNgXz1nLKsxaDQbSDegJfzLF6uh+t1RLmXH+QoA95ZnWTP2z/ut6LKsmXL2HdgHwMffIRhw0cl+mOxpM8Yk6mISA7gG+BJVb3gPsiDqqqIJPoLT5QvTsWK2RenDOSPP5znzJT8hYaG8sMPP9C+fXtGjx5N7dq1+fbbb1mxYgX33XcfW7Zs4Z133mHChAk0adKES5cu4e9//QuMn58fL7/8Mhs2bOCjjz4CYOrUqQDkzp2bWrVq8euvv9KyZUu+//572rVrh6+vL4MHD2bixIlUqFCBdevW8cgjj7BixQpefvllfvzxR4oXL865c+c88ImkO4eBkm7LJVxlxqRNIZfg2E9OshUeCup6hIfC5X+d8rCrTiJ3ZjP4ZIXQIDi9FvzyXe8SGX7N2V+OcuCdBcKuQa6KkKO8K3FzJWp5akD++k4CFpmoebs9XMt++cAn4V3ck1vOnDkZMGAAWbNmTdJ+LOkzxmQaIuKLk/DNVNX5ruLjEfe5iEhR4EQMmx7mehdQcL48rUzJWE3aMsp1cTWl5+lLC4KCgqjlmoX+tttu44EHHqBBgwZ88803ALRq1YrTp09z4cIFmjRpwrBhw7j33nvp3r07JUqUSPBxevfuzZw5c2jZsiWzZ8/mkUce4dKlS/zxxx/07Nkzst7Vq1cBaNKkCf3796dXr1507949+d5wxrUIGCois4EGwHm7n88ku/AwuHLQaQ27uNtpXQs65rSswfWkLSKBO/u307oVfhUu7oGrp52EKqLbZJYCUKi5q2XNx3kWb2dfWfJBtpJOa1muKpC7irMvn2zXy718QXzBy9tzn0kyCA4O5ttvv6VNmzY0TqarjZb0GWMyBXGa9L4Adqjqe26rFgH3A2+6nhfGsPmPwOtuI3u2BUamYLjGeIz7PX3xGTFiBHfccQdLliyhSZMm/Pjjj1Fa++LSpUsXRo0axZkzZ9i4cSOtWrXi8uXL5MmTJ8bjT5w4kXXr1rF48WLq1q3Lxo0byZ8//028s4xFRGbhXIwqICKBOINN+QKo6kRgCdAR2AtcAQZ4JlLjMapwJdC59yzynrQQuHzASdYilsNDnC6QXr43trJd+hdCLzkDjASfAJ+cUVvgcHVqyV3VGYgkX13wzem0kuWs4Era3BK43NWvJ2tefpCtBPjkcEvYMuYUKzfjwoULzJw5kzJlypA3b4wDiieKJX3GmMyiCdAP+EdEtrjKRuEke3NF5AHgP6AXgIgEAA+r6iBVPSMirwDrXdu9HDGoizGZwW233cbMmTN58cUXWblyJQUKFCBXrlzs27eP6tWrU716ddavX8/OnTsjWwnB6ZZ08eLFGPeZI0cO6tWrxxNPPEGnTp3w9vYmV65clC1blq+//pqePXuiqvz999/UrFmTffv20aBBAxo0aMAPP/zAoUOHMnXSp6p3x7NegUdTKRyTUlQh6AiEnHda0S7schKuMNe9aiLXE7fwELi012l1O/ePk+xB1G6NXr5w7Tzkqe60kEW0jIUFQ65KTjLmnqTlq3e9hS1bSWebKImczf6WnFSVOXPmUKNGDRo3bpys84xa0meMyRRUdTWRQ2Pd4PYY6m8ABrktTwYmp0x0xsQsIVMspIYxY8YwcOBAatSoQbZs2Zg2bRoA48aN45dffsHLy4uqVavSoUMHjh693oOwZcuWvPnmm9SqVYuRI29sHO/duzc9e/ZkpVu/2ZkzZzJkyBBeffVVQkJC6NOnDzVr1mT48OHs2bMHVeX222+nZs2aKf6+jUlxqk6SdnI1nPwdQi7AlUNOondpP1w96dTLVdlJyEIuOveh+WR1ts1WErLmuZ7QFWgA/kUhWzHIdWu8A4WYtOPYsWMUKlSIfv36JbjHxM0Q50JQ2pa19tC0H6RJsLPrP/J0CCaZ+fvEmkxlWgHFiumGwYPjr2hTNqQLLVo4zyl9T9+OHTuoXLlyyh4kE4rpcxWRjaoa4KGQ0qyAgADdsGGDp8PIeEIuQOAiOL3eSfAu7XNa78TLGQ3SLy8UvM0ZXCR7SfDJ5Rp8pBz42WjQGd22bdtYsmQJ999/P4UKFYq1XlLOW9bSZ4wxxsRj3DhPR2CMSdNCrzgtdef+duZgCw+Gq2fg/FanGyY4rXJF20O5gVCgsXPPm3fWdD/oiEmadevW8fvvv9OvX784E76ksqTPGGOMiYfbbWrGmMwiNAhO/e48X9rrjEp5fhuc3Uzk3QIRg6FcOwu+eaBoO8hXB3xzORNsl7oL8jeA7KVtkBITo5CQEAYOHEiePHlS9DiW9Blj0gURqQXUAfIBZ4DNqrrZo0GZTGP5cue5dWvPxmGMSUEXdsOBmRB01GmhO7XGSdyKtAFvP8hSCAq3gIpDnVa6iMm6I559c3r6HZh0IiwsjMWLF1O/fn2aNm2aKse0pM8Yk2a55tV7zPUoAuwBLgC5gAoicgwYD3ykqiEeC9RkeK++6jxb0mdMBqLhsOsDOL7SGe3y8r9QuBWU7g0Fm0LdD5wpCGyESpOMrl69ypw5c8iSJUuqjkBsSZ8xJi37B9gIPAisUtVrEStExA9ohjP31N+AjX5hjDEmZuFhsHu8Mxn4hZ1w9RSc+NXphlnpSaf1Lm8t8C/o6UhNBjd//nzy589Phw4d8PJKvQsKlvQZY9Ky7qq6PaYVrgRwObBcRCzhM8YY48xBd2g+hF6Es1ucycbP/uUkelkKQLlBUKChM61Bo2nOvXbGpIIzZ86QO3duunTpQrZs2ZJ1Dr6EsKTPGJNmxZbwxVBvR0rHYoxHJPeUHgnYn4gwbNgw3n33XQDeeecdLl26xJhkjuX1119n1KhRkcuNGzfmjz/+SNZjmEwkNAj+fAgOfAn+RaBkD/DJ7rTeFWnrPOeq5EwobkwqO3jwIHPnzqVnz56ULu2ZCw32m2+MSTdEpClwH1BUVTuLSF0gu6qu8nBoxmQYWbJkYf78+YwcOZICBQqk2HGiJ32W8JmbcvkQHP3Bac0Luei07uUsDx22QN6ano7OmEg7d+7ku+++o1u3bh5L+ADszlRjTLogIvcAC4FgnHv5ABR42WNBmUzj00+dR2bg4+PD4MGDef/9929Yd/LkSXr06EG9evWoV68ev//+e2R5mzZtqFq1KoMGDaJ06dKcOnUKgK5du1K3bl2qVq3KpEmTABgxYgRBQUHUqlWLe++9F4AcOXIA0KdPHxYvXhx5zP79+zNv3jzCwsIYPnw49erVo0aNGnzq+oEcPXqUZs2aUatWLapVq8Zvv/2Wch+O8aygY7CqO3ydBxaWgp3vOaNm5q8HjWdC+02W8Jk059SpU9x7772UL1/eo3FYS58xJr14HminqhtEpJ+rbCtQ1YMxmUyiUiVPR5C6Hn30UWrUqMGzzz4bpfyJJ57gqaeeomnTphw8eJB27dqxY8cOxo4dS6tWrRg5ciRLly7liy++iNxm8uTJ5MuXj6CgIOrVq0ePHj148803+eijj9iyZcsNx+7duzdz587ljjvu4Nq1a/z888988sknfPHFF+TOnZv169dz9epVmjRpQtu2bZk/fz7t2rXj+eefJywsjCtXrqT0x2NSS9g1uLjLGXwlcCHsGgf560OzBc6zT3ZPR2hMjFSVlStXUq5cuVSbkiE+lvQZY9KLYqq6wfVaXc+hgLeH4jGZyHffOc+dO3s2jtSSK1cu7rvvPsaPH0/WrFkjy5cvX8727ddvtb1w4QKXLl1i9erVLFiwAID27duTN2/eyDrjx4+PXHfo0CH27NkT5zDlHTp04IknnuDq1assXbqUZs2akTVrVpYtW8bff//NvHnzADh//jx79uyhXr16DBw4kJCQELp27UqtWrWS86MwnnB4Cfw1Es797SwXaAS+uaH1b1AobXyBNiY2YWFhfPfdd5w6dYr69et7OpxIlvQZY9KLfSLSWFXdb/xpDOzyVEAm83CNaZJpkj6AJ598kjp16jBgwIDIsvDwcNauXYu/v3+C9rFy5UqWL1/OmjVryJYtGy1atCA4ODjObfz9/WnRogU//vgjc+bMoU+fPoBz5fzDDz+kXbt2N2yzatUqFi9eTP/+/Rk2bBj33XffTbxTk2ac3wlbX4b/ZkGFIdByKWQt6umojLkpixcvJigoiPvuuw8/Pz9PhxPJ7ukzxqQXrwILReQFwFdEngZmY/f0GZMi8uXLR69evaJ01Wzbti0ffvhh5HJE98wmTZowd+5cAJYtW8bZs2cBpzUub968ZMuWjZ07d7J27drIbX19fQkJCYnx2L1792bKlCn89ttvtG/fHoB27drxySefRG6ze/duLl++zH///UfhwoV58MEHGTRoEJs2bUq+D8GkDlXYPx0WV3YGZWm/Cep9bAmfSVcuX75MaGgoLVq0oHfv3mkq4QNr6TPGpBOq+q2IXAYeB/4DWgEDVPUnz0ZmTApK7ikbbtLTTz/NRx99FLk8fvz4yPv9QkNDadasGRMnTmT06NHcfffdfPnllzRq1IgiRYqQM2dO2rdvz8SJE6lcuTKVKlWiYcOGkfsaPHgwNWrUoE6dOsycOTPKcdu2bUu/fv248847I784DRo0iAMHDlCnTh1UlYIFC/Ltt9+ycuVK3n77bXx9fcmRIwfTp09PnQ/HJI/902HDIxB+DW5bACW7ejoiY27a6dOnmTFjBm3atKFKlSqeDidGoqrx1/KwrLWHpv0gTYKdXf9R/JVMuuLvQ+rOMJoOBBQrphsGD46/ooe/1JuEadHCeV65MmWPs2PHDipXrpyyB0kBV69exdvbGx8fH9asWcOQIUNiHKTFU2L6XEVko6oGeCikNCsgIEA3bNgQf8WkCj4Bq3vBiV+h4TQo3Ru8s6T8cY1JZoGBgcyePZtWrVpRp06dFD1WUs5b1tJnjEmzRKRYQuqp6pGUjsUYE7uDBw/Sq1cvwsPD8fPz47PPPvN0SCatUoUjP8BfI5zX3Y5C1iKejsqYRDt48CBdunShYsWKng4lTpb0GWPSskCuj9QZE3Gtj3cETxGZDHQCTqhqNVfZHCBiMP48wDlVrRXDtgeAi0AYEGqtA5nPl196OoK0rUKFCmzevNnTYZj0YOvL8M8YqDISqo4E35yejsiYRNm4cSN58uShcePGng4lQSzpM8akZWWTcV9TgY+AyBt+VLV3xGsReRc4H8f2LVX1VDLGY9KRkiU9HYEx6dyZjfDvl7DrA2j5IxRt6+mIjEmUiDn4/vnnH/r27evpcBLMkj5jTJqlqv8l475WiUiZmNaJiAC9cAaHMeYGc+Y4z717x13PGBODP4fA3olwywBnsBZL+Ew6tnLlSvbu3cvAgQPJkSOHp8NJMEv6jDHphojcCrQACsL1wWNUNanTNtwGHFfVPbGsV2CZiCjwqapOiiW+wcBggFK5cycxJJOWfPKJ82xJnzE36b+5cOBL6HYMshb2dDTGJNq1a9cAqFOnDk2aNElzUzLEx5I+Y0y6ICJ343TR/Buo4XquCaxKht3fDcyKY31TVT0sIoWAn0Rkp6recFxXMjgJnNE7kyEuY4xJn1Rh+//g7+eh3ieW8Jl07fLly3z11VfUqFGDBg0aeDqcRLHJ2Y0x6cXzQD9VrQdccT0/DCRpJmYR8QG6A3Niq6Oqh13PJ4AFQP2kHNOYtMzb25tatWpRs2ZN6tSpwx9//JHixyxTpgynTtktsxnG+e3wQ03Y9hq0WgHlEzB9jTFp1JkzZ/jiiy8oX7489eun33//1tKXBE3qlOPJ+1pTp3JJihXKw4MvfcmM79ZFrg/aHPN8dBPnrOKpN+cmax13Hz7fh0F3NWXkewsY9+XPN/WeTNw2bljPtClfsH37Nk6eOMHLr77Bnd26x1j35TEv8c3Xcxj2zLPcP+ABAM6fO8fHEz5k7ZrfOXrkCHny5qVZ8xYMffxJ8uTJm5pvJT0qBXwdrWw6cAh4Ngn7bQ3sVNXAmFaKSHbAS1Uvul63BZLandSYNCtr1qyRc+z9+OOPjBw5kl9//dWzQUUTFhaGt3e8g/YaTzjyA6zuDWX7Qts14JPd0xEZkyR79+6lcePGBASk74G7raUvCXJky8L2vUd45u15XAm6dsP6Mq1HRnl0f3wiAN/8tCnZ60To1roWAdVKc+TEueR8q8blypUrlK9QkedGPI+/v3+s9X76cSlb//mbgoUKRSk/cfIEJ04c56lhw5m34Dtef/NtNm7YwHPDn07p0DOCc0DEjXLHRaQykA9I0DcKEZkFrAEqiUigiDzgWtWHaF07RaSYiCxxLRYGVovIX8CfwGJVXZqkd2JMOnHhwgXy5nUuSKkqw4cPp1q1alSvXp05rtFtVq5cSadOnSK3GTp0KFOnTgWcFrzRo0dTp04dqlevzs6dOwE4ffo0bdu2pWrVqgwaNAjV672hu3btSt26dalatSqTJl2/fTZHjhw8/fTT1KxZk9dee42uXbtGrvvpp5/o1q1bSn0MJiHCw+D3e2FlR6j3sfOwhM+kY3v27GHXrl3Ur18/3Sd8YC19SfLj6u38uHo7AJPG9rth/fHTF6Msd2pRnd0HjrN6495krwNQqmhe3hl+Fx0f/pCFHz2SuDdl4nRbs+bc1qw5AC8+PzLGOkeOHOZ/b77GpM+n8ujDD0ZZV6FCRd7/4HrLbanSpRn2zLM89shDXLp0KV2NAuUBy4FuwBRgrms5BPghIRur6t2xlPePoewI0NH1ej/OvYMmE5s3zzPHbdHixrJeveCRR+DKFejY8cb1/fs7j1On4K67oq5buTL+YwYFBVGrVi2Cg4M5evQoK1asAGD+/Pls2bKFv/76i1OnTlGvXj2aNWsW7/4KFCjApk2b+Pjjj3nnnXf4/PPPGTt2LE2bNuWll15i8eLFfPHFF5H1J0+eTL58+QgKCqJevXr06NGD/Pnzc/nyZRo0aMC7776LqlK5cmVOnjxJwYIFmTJlCgMHDoz/zZmUce08/NYDzv1l0zGYDGHz5s38/PPP9OnTx9OhJBtr6Usl2bP60bNdXabMj/3eiKTU8fb2YtobA3jz86Xs+vd4ssVtbk5oaCgjhj/N4IeGcEu5cgna5tKlS/j5+cXZcmhAVQeq6hTX4mhgOPAm0N9jQZlMo0AB55EZRHTv3LlzJ0uXLuW+++5DVVm9ejV333033t7eFC5cmObNm7N+/fp499e9u9MFvm7duhw4cACAVatWRc5vdccdd0S2JgKMHz+emjVr0rBhQw4dOsSePc6gut7e3vTo0QMAEaFfv37MmDGDc+fOsWbNGjp06JCcH4NJqEv7YV4eCLkAnfdYwmfSvY0bN7Jq1SoGDBhAiRIlPB1OsrGWvlTSu0M9/Hy9mfH9uhSp8+LDd3Dq3GU++3p1ssVsbt4nEz4kT5489OpzT4LqX7hwgY8//IDud/XCx8f+HBNKnb5gX3k6DpN5uHor0r9/6h43rpa5bNniXl+gQMJa9uLSqFEjTp06xcmTJ2Ot4+PjQ3h4eORycHBwlPVZsmQBnKQtNDQ0zuOtXLmS5cuXs2bNGrJly0aLFi0i9+fv7x/lPr4BAwbQuXNn/P396dmzp51DPWXNfVDsDmjxvacjMSZJwsPDCQ0NpUKFClSqVCnD9b6ylr5UMrB7Y75f+Q+nzl5K9jq31a1Avy4NGDJ2ZrLGbG7O+j/XsfDb+Yx55fUE1b9y+TKPP/owhQoX5qmnh6dwdOmfiCwTkVbRym4XkQR17zQmKaZOvZ74ZSY7d+4kLCyM/Pnzc9tttzFnzhzCwsI4efIkq1aton79+pQuXZrt27dz9epVzp07x88/xz+AWLNmzfjqK+e6zQ8//MDZs2cBOH/+PHnz5iVbtmzs3LmTtWvXxrqPYsWKUaxYMV599VUGDBiQPG/Y3JyD8+Dk71A/xqlLjUk3QkJCmDNnDqtXryZXrlwZLuEDa+lLFTUqFqdu1dK89OF3KVKnWUAFihTIxb/LXoss8/Hx5tUn7mTovS0o3/7FpL8JE68N6//k1MmTtG7RNLIsLCyMce+9w4wvp/HTiuvTul25fJlHhzhDWH84YWLklXATpzrcOCffb8Qx1YIx5uZF3NMHzuAt06ZNw9vbm27durFmzRpq1qyJiPDWW29RpEgRAHr16kW1atUoW7YstWvXjvcYo0eP5u6776Zq1ao0btyYUqVKAdC+fXsmTpxI5cqVqVSpEg0bNoxzP/feey8nT56kcuXKSXvT5uaFh8HfL0CdcZCtmKejMSbRrly5wqxZs8iXLx/Nmzf3dDgpxpK+VDCwRxP+DTzFinU7U6TOpLmrWLB8c5Sy7z5+lLlLNzJ5/u9JC94kWK8+99C6bbsoZUMGP0CHjp3ocVfPyLLLly/xyEMPgiofT/qcbNltdLMECgd8Aff+Yd6AeCYcYzKmsLCwGMtFhLfffpu33377hnVvvfUWb7311g3lEffwAQQEBLDS1d80f/78LFu2LMbj/PBDzI33ly7d2Atm9erVPPjggzHUNinu6A8QegUq2sBxJn3bs2cPpUuX5vbbb0ck436lsKQvCbJn9aNcyYIAeIlQsmhealQsztkLVzh0zOmqktXflz4d6vHetOWx7iepdU6evcTJaF1CQ0LDOH7qAnv+O5GYt2ZiceXyZQ4ePAiAajhHjx5h544d5M6dm6LFipE/f/4o9X19fClQoABlyt4COAnfww8+wKVLlxj34QSCrgQRdCUIgNy5c+Pr55e6byh92Qg8Brh/sxxKEidnN8akT3Xr1iV79uy8++67ng7FY0SkPfABzgWwz1X1zWjrSwHTgDyuOiNUdUn0/STK7o+g8rPg5ZssuzMmtR09epSzZ89Ss2bmGKDbkr4kqFOlNMs+fyJy+aUhnXhpSCe+XLSWwaNnAHBX27pkz+rHl4tivy8hueqYlLdt21YGDbgvcvmTCR/yyYQP6XJnN155/c04tnRs37aNv//aAkCXjlFbBT+fMp169Rska7wZzHPAShHpAewGKgCVgBaeDMoY4xkbN270dAgeJSLewASgDRAIrBeRRaq63a3aC8BcVf1ERKoAS4AyST74znFwYhU0nBJvVWPSor1797JgwYIoc4xmdOI+IWpalbX20LQfpEmws+s/ir+SSVf8fVKni6WIFAH64XxpOQDMUNWjqXHsmxVQrJhuGDw4/opjxqR4LCbprlxxnrNlS9nj7Nixg1tvvTVDdzFKbarKzp07b7jvT0Q2qmq6nXFZRBoBY1S1nWt5JICqvuFW51Ngv6r+z1X/XVVtHNd+AwICdMOGDbFXCA2Cb/LB7b9AgbjvuTQmLdq1axffffcdvXr1iryfOL1IynnLWvqMMemGqh4DbryhyJgUltLJXgR/f39Onz5N/vz5LfFLBqrK6dOn0/Q8qOL8oIsk4gJWceCQ23IgEL27yBhgmYg8BmQHWscSw2BgMBD/l+B/p0GeWpbwmXRHVQkNDaV48eLcf//9FCxY0NMhpSpL+owx6YKIPAqsVtW/RKQuMB9nUJfeqhrHZWljku7jj53nR1J4zIoSJUoQGBgY57x45ub4+/unyQmWRSQHzv149wJhQHYR6QrUVNWxyXSYu4Gpqvquq6XvSxGppqrh7pVUdRIwCZyWvjj3uPczqPxMMoVnTOoIDw9n6dKlhIaG0qVLlww5JUN8UiXpE5FbgTtxrkoBHAYWqeqO1Di+MSZDeJrr0zO8BswGLgLvAhl3jGWTJsyd6zyndNLn6+tL2bJlU/YgJq14FygMNAEiRmlbD7wOJCTpOwyUdFsu4Spz9wDQHkBV14iIP1AASNwob6c3wIUdUKpn/HWNSSNCQkKYP38+V69epVevXp4Ox2NSfHJ2EXkO58uZAH+6HgLMEpERcWw3WEQ2iMiG0FPbUjpMY0zal19VT4lIFqARMBp4A6ju2bCMMSZROgH3qupGQAFU9TCQ0Env1gMVRKSsiPgBfYBF0eocBG4HEJHKgD+Q+Gbk7W+4Ruy0jmIm/di/fz++vr7ce++9abqrd0pLjb/aB4CqqhriXigi7wHbgBiHPHTvamADuRhjgEsiUgwnyftbVYNdX3S8PRyXMcYkhhcQ5F7g6vJ544SEMVDVUBEZCvyIcx6crKrbRORlYIOqLsLpIfGZiDyFk1j218SO4KcKgYug1v8Stbkxqe3cuXMcOXKEKlWqULFixUx/n3SKt/ThTKgc01Wroq51JpUFbf6Ibq1reTqMFLP+z3XUrFqJs2fPeDqUROvQphXTpnwRZ52FC+bTMKB2KkWUJkwF1gFf4sw7BVAf2OupgIwxJglWAyOjlT0G/JLQHajqElWtqKrlVPU1V9lLroQPVd2uqk1Utaaq1lLVZYmO9sRK8C8EOcolehfGpJZjx44xefJkLl68CJDpEz5InZa+J4GfRWQP10eZKgWUx5lYOdGCNsc99L/7fHkprVTRfOxa8jKnz12mSufRXLgUHLnux8+eYPveIzz1v69TJZYIk8b2JX+eHPR4YmKU8jKtR3L2QlAsW6UNL44awaKFC24onzPvW26NNuy2JzzQvx8b1v8JOPfgFC1WnDu7dmPAAw/i7Z30hqeZc+aRNWvWyOWaVSvxznsf0KZd+8iydh060rRZ5rmVTVWfF5GVwDVV/dVVfBWwEQWMMenRMGCFiPQFcojIP4Af0MqzYcVizydwy0CwL88mjTt06BCzZ8+mY8eOVK1a1dPhpBkpnvSp6lIRqYhzRd59IJf1qhqWlH2XaX39AlmHZtX45KV7o5QFXY3SoxQfHy9CQ1O2cTGbvy/PDGjLSx9G71afdhw/fdHTISRIw0aNee2Nt6KU5cmb10PR3OjObt15/IlhXL12lVUrV/K/N17Fy8uLgYMSMDdbPPLlyxdvHX9//0zXN11Vf4q2vN5TsZjMZeVKT0dgMhpVPSQi1XDu7SsL/Ad8r6pp76ps6GUI/Ba6/OvpSIyJU1hYGPnz56dPnz6ULFky/g0ykdTo3omqhqvqWlX9xvVYm9SED5zkJeJx/mJQlLIsfr4c/+0derWvyw+fPsaZNe8xqEdT+nZuwMnf342yn9vqViBo80fkz5M9sqxhzbIs+/wJTv/xHvt+fJUPRvUmZ/b4v2B/POtXHr27BcUK5o6z3rD7W7Nt0WjOrHmP9XNH0adjvSjr61UrzR9fPcfZte+zZtZztGtahaDNH3Fb3QoAeHkJn4y+hx3fj+HMmvf4Z+FLDLu/dWTz9fMPdaRfl4Z0bFaNoM0fRdnWvXvnL1OH8eawblGOnTO7P2fWvMedrWoC4OvjzauP38nepa9w+o/3WD1jOK0bXW9t8/Hx4t1n72L/stc4t+599vzwCq883iXezyo+vn5+FChYMMrDx8eH6VOncFe3zjQIqEXrlrcx5qXnuXDhQqz7uXjxIqNGDKfFbY2oV7s6HdvdzozpU6Osf3n0i7S4rRGN6tVm4P192bb1n3jj8/fPSoGCBSlevAR339uXBg0b8cuKnwG4cP48L4x8jqaN6lG/Tg0GP9CfvXv3JDgm9+6dHdo4F32fGfYENatWilx279554MC/1KxaiT27d0WJcd7cOTRv0oCQEOcCyL69exk6ZDCN6tWmxW2NeO6ZYZxyGxp+z+5dPDjwfhrXr0PDgNr07NaFP9etjfezSA9EZLKInBCRrW5lY0TksIhscT06xrJtexHZJSJ74xqAyhhjEkpE+qnqVdf3ondU9WtVDRKRez0d2w1O/gG5q0K24vHXNcYDVJXff/+defPmkS1bNkv4YpDhh196+bEujHx/AQ+P/YrQ0DBaNqgU7zZVyxfju4+H8urExQx5+Svy5crG28Pv4tMx93LP8Ljvs5q/fDPNAirw4iN3MGTsVzHWGfNoZ7q1rsWTb85l94ETNKxRlgkv3c25C1dYunob2bP68c34h1mxdhcPvDCNogXz8PbwHlH24eUlHDlxnr7PTubk2UvUq1aaj168m9PnLzPt2zWMm76cW8sWJm/u7DzwgnP705nzV26IZdbi9Tw3qB0j3/+WiHu7u95ei+BrofzwmzNq6qSxfbmlRAHuHzWVw8fP0b5pVb754CGa9n2bf3Yf5tG7W9ClZU36jZjMwSNnKF44DxVKF473c04sLy9h+HOjKFGyJEePHOHN11/hzddf4fU3Y56z+6Px49i7ezcfTviU/AXyczgwMPJ+P1Vl6JDB5MyZkw8nfEru3LlZtHABDw68n4WLl1KwYKEEx5UlSxYuXDgPwIvPj+DAgX/54MOPyZUrFx9+8D6PPDSIRYt/xN/fP86Yops5Zx4tb2vE6LGv0qx5C7xi6D5apkxZqlarzuLvv+PJYdd/x5cs/o627Trg6+vLyZMnGHj/vXTtfhfDnnmO0NAQPvxgHE889ghffjUHLy8vRjz7DBUrVWLm7K/x9vZhz57dZMmSJcGfQRo3FfgImB6t/H1VfSe2jUTEG5gAtMGZ/Hi9iCxS1e0pFahJe95x/YY8Y52JTfKZgHOPcnQfAjNTOZa4Hf8FCjb1dBTGxCg8PJwff/yRAwcOcO+9ae+aSVqRKi19nvTJ7F9ZsHwL/x05zeET5xK0zVP33868ZRv54MsV7Dt4kvVb/+Px12bTrXVtCuaNfzLHUR98S99ODah8S5Eb1mXz9+Pxvi155OWv+OmPHfx35DRzlm5gyvw/eKh3MwD6dKyHt5cXD4+dyY79x1ixbidvff5jlP2EhobzyieL2bj9IAePnuGbnzbz+bzV9GpfF4DLQdcIuhrC1Wuhka2fIaE3Nq7OW7aJAnlz0LxehciyPh0DmP/TZq6FhFK2RAF6ta9L3+cm8/umfRw4fJqJc1bx4+ptPNCjCeDcz7jnvxP8vmkfh46dZe1f//LloqS3Dv2x+jcaBtSOfDzy0CAA+t7XnwYNG1G8eAkC6tXnqWHDWbb0B8LDY+66e/TIYW6tUoXqNWpQrFhx6tVvQNt2HQD4c91adu3cyTvvj6d6jRqUKl2aoY8/SYkSJfl+0cIExRkeHs7vv63ij99XU79BI/777wArf1nBS2NeoW5APSpUrMRrb77N5UuXWPL9d/HGFF1EV8+cOXNSoGDBWLt+3tG5C0uXLI5M3o8eOcKmjRu4o7PT6jp39iwqVrqVp54ezi3lylGx0q289sb/2PrP32zbujUyrkaNmlD2lnKUKl2a21u3oWatjDFYjKquAhIzuk99YK+q7lfVazhT0NyZrMGZNO/7752HMcnohpvjRKQMEJr6ocRBFQ7MhEItPB2JMTE6dOgQJ06cYMCAAeTKlcvT4aRZGb6lb+P2gze9Te3KpShXsgB3ta0bWRZx3/ItJQty8mzcoymv3riXn9bs4OXH76Tnk59GWVf5liJk9fdj4YRHcB802dfHi/+OON9HK5UpzPZ9Rwl2uyfxz60HbjjOoLua0r9bY0oVzUvWLH74+nhx8OjZm3qvZ85fZtkfO+jToR4r/9xN0YK5aR5QkdcnfQBA7VtL4uXlxaZvXoiyXRZfH1au3w04A+Z8/8lj/LPwJZav2cmPq7fx4+/bI5OPxKpTN4CXxrxy/Ziu+9fWrV3D5M8nsX//Pi5dvEh4eDghISGcOnWSQoVubGHs1edunn7qCXZs20bDxk1o3qIlAfXqA7Bj+zaCg4No0bRRlG2uXbvKoUOHbtiXu2++nsuibxdEdp3s1KULDz8ylHVr/sDLy4saNWtF1s2ZMyflK1Rk37698caUWO073MF7b/+PTRs3UDegHj8s+Z7iJUpQq3adyPe6aeOGGEf8DDx0kOo1atDv/gGMHf0CixYuoEHDRrRu05ayt2T4kdqGish9wAbgaVWN/kdUnOuDUIHT2tcgph2JyGBgMECp3HF38TbGZE4iEoIzfYK3iFyLttob+CT1o4rD4UUgXlDCrnWZtCUoKIgDBw5QuXJl7rvvPhuhMx4ZPum7EhT1fBquesPAU74+UbvLeYkwZcEaPpy54ob9HTlxPkHHfXH8ItbNHkGT2lG/MHt5OQfv8cSnHDoWtdEhNIaWuNjc1bYObz/Tg5HvL2DtX/u5cDmYh3s1o4vrPrybMXvxn0x48R6eeGMOPdvVJfD4WX7ftC8y3vDwcJr2feuGlsLgYCfZ2bIzkFvveIk2jSvTon4lPnu5H//sPswdQz5KUuLnnzUrpUqXjlJ25MhhHnvkIbrf1YtHhj5O7jx52LF9OyOGD4tMvqJreltzlv60gtW/reLPtWsZOuQh2rRrzyuvvUF4eDj58xdgyvQbe9JkzxF3q267Dh14eMhQ/Pz8KFioUIJG7Yw4IcUVU2Llz5+fho0as/j776gbUI8li7+j4x2dI9eHh4dzW7PmDHvmuRu3LZAfgCGPPkbHTp1Z/dsq1vy+mokfT+CF0WPo1v2uRMeVFCIyOSH1VHVgIg/xCfAKzhewV4B3gcTuK8r8ogHFitn8osaYmLTGaeVbArh38QgHjqnqnhi38pStr0ClJ21CdpOmnD9/npkzZ1K+fHkqV65sCV8CZLq/4FNnL5E9axZyZvfn4mVnWoWalaLemLxl5yGqlCvK/kOnEn2cbXuPMPP7P3ntya5cvXa9p8aO/ccIvhpCqaL5+NXVUhbdrgPHubdzA/yz+Ea29tWrFjX5aVy7HOu3HmDinFWRZWVLFoxS51pIGN7e8f8RfP/rP0x4ETo2q0bvDgHM+WFD5LotOwPx8vKicP5crNoQ+/+hS1eusmD5FhYs38KMRWtZ9eVwypUsyN6DJ+I9/s3YvnUrISEhDH9uZGSStSoBw+rlzZuPzl260rlLV5rc1owRw4fx4uixVK5SldOnT+Hl5UWJm7zpN0eOnDckpQBly5UjPDycv//aQt0AZ4CeS5cusXfPbu7s1j3emPz8/G7Yp4+PL2GxdF91d0enLrzx2ivc1bMXe3bv5p33x0euq1ylKsuW/kDRYsXw9fWNdR+lS5ehdOky3Nv3Pl59eTQLvpnnsaQPcL/S4A/0Bv4E/gXK4LS4zU7szlX1eMRrEfkMiKkD32HA/ZejhKvMGGNuWsSUMyJSTlWPejqeOJ3fDud3QIWHPB2JMZFOnTrFl19+ScOGDWnUqFH8GxggE9zTF936fw5w6cpVXn6sC7eULEDX22sxuNdtUeq8O/UnAqqWZvzzfahZqQS3lCxAh9uq8eHzfW7qWK988j01KhaPkrBdunKVcdN/5o2nunHfnQ25pWQBalQszqC7mjKwu3OP3JwfNhAWHs7HL97NrbcUoWWDSgwf2A4AxWk82PPfCWrdWpK2TapQrlRBRjzYntvqlI9y/P+OnKZquWJUKF2I/Hmy4+MT84/76rVQvv15C88Nak+dKqWYteT6KPh7D55g1uI/+ezlfnRrXYsyxfNTp0opnux3e+Tono/3bUWv9nWpVLYwt5QsQO8OAZy/GMThEzfX1TQhSpUuTXh4ODO+nEZg4CF+WPw9M7+cFuc2Ez78gBU/L+e//w6wf98+fl6+jBIlSuLn50fDRo2pVbsOTzz2CKt/+5XAwEP8tWUzH380nk0bN8S539iULl2Glq1u55WxL7Fp4wb27N7FqOeeIXuOHJEtb3HFFJNixYvz59o1nDp5kgvnY29tbnl7a0JDQxjz4vNUrVadMmXKRq7rffc9XLx0kWefeYq///6LwEOHWLvmD14e/SKXL18iODiY118Zy/o/13H4cCB///0Xmzdt4pZynuveqaoPRjxwWuMeUNWmqtpPVW8jCa1yACJS1G2xG7A1hmrrgQoiUlZE/IA+QNqdk8WkiKxZnYcxyUVVj4pIftfowP1E5L6Ih6djixT4LZQbCN6Za3ogk3aFh4eTI0cOOnbsaAnfTcp0LX1nL1xhwPNTef3Jbtx/Z0NWb9rH2I8XM+W1+yPrbN1zhDaDxjH6kU4s+/wJvL29+DfwNIt++eumjhV4/Bwfz/qVpwe0iVI+9uPvOXHmIk/edzvjR/XmwuVg/t51mPemLgecxPCuJz7lg1G9WTvrOXbsP8Zrny5h1juDuOpq+ft83mpqVCrO1Nf7IwLf/ryFD2b8zP13Xv8DmLLgD5oFVOD3mc+SM7s/bQd9wG8bY26tm7VkPfd3bcTmHYfYuf9YlHWDx8zguQfa89oTXSleOA9nzl9hw7b/+HWD01J58XIwT97XmvKlCqKq/LUrkK6PfUxQcMzdLZOiYqVbeXbk80z54jMmjB9HzVq1GTb8WZ59+qlYt/Hz8+PDD97nyOFA/PyyUKNmTcZPcCasFxEmTJzER+PHMXb0i5w5fYb8BfJTq3YdOnfpmug4X371Dd5683UeHzqEa1evUqt2HT7+9PPIefXiiikmTw9/jnffepN2rVtQqFBhfvjpxq7HAFmzZqVV6zZ8v2ghz458Psq6QoUKM23GLMa//x6PPDSIa1evUqRoURo1boqfr5NsXrhwgZeeH8nJkyfIkycPzZq3ZNjwG7uDekgXoH+0slk4I3LGS0RmAS2AAiISCIwGWohILZyE8gDwkKtuMeBzVe2oqqEiMhT4Eed+m8mqui2pb8akLz/84OkITEYjIq2Bb4BrQB7gnOv5X24cZdgzrgRCriqejsIYALZu3cqmTZvo168flSrFPxq/iUqSOthGashae2jaDzKFdWpRnTnvPkip20dy+txlT4eTJGfXJ+g7uklH/H1uHIUuuYnIXmCwqq5wK2sJfKGqt6T08W9WQLFiumHw4PgrjhmT4rEYY24kIhtVNcCDx98AzFLVd0XkrKrmFZGXgEuq+p6n4goICNANG1w9XX66DaqMgOJ3eCocYwBYs2YNa9eu5Z577qFw4ZSbFiytS8p5K9O19KUX93ZuwIHAUxw6dpaq5Yvx9jM9WLxqa7pP+IxJgteB70Xka5xWuTLAXcBjHozJZBKvuAYSfvFFz8ZhMpQKwDjX64gLZ/8D9gAeS/oihVyCk6uhYBNPR2IyuRMnTrBlyxYGDhxIbhsZO9Es6UujCufLyYsPd6RIgVwcP32RH37bygsfJGzeOGMyIlWdLCJ7gH5APZzBVDq45t8zJkX9/LPzbEmfSUZXgCyu59MiUgo4C+T1aFQRLuyAPNXBL4+nIzGZVGhoKPv376dixYo89NBDeHlluqFIkpUlfWnUe9OW89605Z4Ow5g0RVV/A37zdBzGGJMM/gC6Al8BP+AMEHUVWOPBmK47uxlyZPh5Wk0aFRwczJw5c8iaNSsVKlSwhC8ZWNJnjEk3RKQpcB9QVFU7i0hdILu19hlj0qG+XB9F/RngaSAnaaFrJ8CJ1ZC9jKejMJnQpUuX+PLLLyldujTt27e3OfiSiaXNxph0QUTuwbkSHgw0cxUr8LLHgjLGmERS1SBVvex6Hayqr6nqCCBtDEsYuABK9/Z0FCaTUVVnSq2GDenQoYO18CWjBH2SIvKoiNR0va4rIv+JyD4R8dioV8aYTOd5oK2qPg5EzFS/FajquZBMZpE/v/MwJjmISA4RqSMi+d3KaorID8BPHgzNEXYNQi859/QZk0r+++8/pk2bho+PD7Vr17YWvmSW0O6dTwNzXK9fA2YDF4F3geYpEJcxxkRXTFVd44gTMY1LKM7cecakqG++8XQEJqNwTTXzLU5XzmARuQtoBAwHZgC3ei46l7NbwL8Q+GT3dCQmk9i+fTuLFy+me/fu1rqXQhKa9OVX1VMikgXnxNQVCAGGpVRgxhgTzT4Raayqf7iVNQZ2eSogY4xJhFeBScAUYDDwJbADqK6qezwZWKTz/0CO8p6OwmQSFy5c4KeffqJv374ULVrU0+FkWAlNpS+JSDGgBfC3qgbjXF23K+zGmNTyKrBQRF4AfEXkaZxeB3ZPn0lxI0c6D2OSwa3AKFXdDowC8gA90krCd+QIFAzog1f71RQsCKNHw6VLno7KZESqyv79+8mVKxePPvqoJXwpLKEtfVOBdTjzyYxyldUH9qZATMYYcwNV/VZELgOPA/8BrYABqur5+19MhrcmbQyibzIGX1UNAVDVKyJyXlWPezqoCMeOgarTrfPUKXjrLad789q1kCOHh4MzGUZYWBgLFy7k7NmzlC5dGh8fm1AgpSXoE1bV50VkJXBNVX91FV/FGWLYGGNShSvBsyTPGJOe+YrI3YDEsoyqfuWRyADVqMvBwbBvH7z9Nowd65mYTMZy9epV5s6di6+vL/fddx/e3tZxMDUkOK1W1Z/EUVRVj6rq+pQMzBhj3InIJmCKqn4YrXy1qjb1UFjGGHOzjgOvuy2firasOBO2pxnBwfDJJ5b0meTh5eVF+fLladCggQ3akooSOmVDDhH5HAjC1aVTRLqKyOiUDM4YY9xUBYaJyDvRymt4IhhjjEkMVS2jqmXjeNzi6Rhjcvq0pyMw6d2pU6eYPn06AI0aNbKEL5Ul9NN+FygCNAGuucrWAzZrpzEmtVzFGT24lYjMFhFfTwdkMo8SJZyHMZmVzVNpkuLQoUNMnTqVGjVq4Otr/749IaHdOzsBVVT1vIgogKoedo3oaYwxqUJVj4lIc2AesFxE7vR0TCZzmDHD0xEY4zn+/jBkiKejMOlVcHAw8+bNo2vXrpQvb1OBeEpCW/q8cLp2RhKRHIAN4muMSS0CoKoXgY7AfuAPwC4ZGmNMMhGJuuzvD+XKwfDhnonHpG+BgYH4+/vzyCOPWMLnYQlN+lYD0Wcoegz4JXnDMcaYWEWO2qmqYao6AJgLHPNcSCazePJJ52FMRlekcBgFc53CywsKFoRnn7XpGszNU1V+/vlnFixYwNWrV8mSJYunQ8r0Etq9cxiwQkT6AjlE5B/AD2eeLGOMSXGq2j2GsjHAmIRsLyKTcbqqn1DVaq6yt4HOOPcq78OZ9+9cDNseAC4CYUCoqgYk5j2Y9GvLFk9HYEzqKFY0lA2j68Od+z0dikmnwsLC+O677zh16hQDBw60hC+NSOg8fYdEpBrOl6MyOBMjf6+qQXFuaIwxSSAiPVX1a9fre2Kppqo6KwG7mwp8BEx3K/sJGKmqoSLyP5weDc/Fsn1LVT2VsMiNMSZuIuKNc865HyikqrlFpB1QVlUneiyw8DAIu+yxw5uMIU+ePHTs2BE/Pz9Ph2Jcbmaevqs4gycYY0xqGQ187Xr9Wix1FIg36VPVVSJSJlrZMrfFtcBdiYjRGGMS4xWgNc6Fpsmust3Am4Dnkj4UsqfJWSNMGnfp0iW+//57unTpQosWLTwdjokmQUmfiEyKbZ2qDk6+cIwx5rqIbpiu12VT+HADgTmxhQIsc41e/KmqxnhOFJHBwGCAUrlzp0iQxpgM4x6gkaoedc2FDHAAp0eV52gY+GTzaAgm/Tl9+jQzZ86kRo0aZM2a1dPhmBgkdCAX32iP0kA/wH6qxph0T0SeB0KBmbFUaaqqdYAOwKMi0iymSqo6SVUDVDWgYDb70pSRVKzoPIxJRtmAE9HK/IDghGwsIu1FZJeI7BWREbHU6SUi20Vkm4h8laCowkMhS8EEVTUGIDQ0lJkzZ9K0aVNatGiBRB8C1qQJCb2nb0D0MhHpBrRN9oiMMcYlrl4G7pLS40BE+uMM8HK7qmos+z/sej4hIguA+sCqxB7TpD+TEvSbaMxN2QQMAD53K7sH+DO+DV33A04A2gCBwHoRWaSq293qVMC5Z7CJqp4VkUIJiir8GvjmSvCbMJnb8ePHKVSoEIMGDSKbXexM0xLa0heTb4HeyRSHMcbEJHovg9geiSIi7YFngS6qeiWWOtlFJGfEa5yLXVsTe0xjjHF5BnhTRJYD2UTkO+B1Yh9Myl19YK+q7lfVa8Bs4M5odR4EJqjqWXAuWiUsLAVJytdDk1ls3LiRGTNmcOnSJUv40oEED+QSgw5Em7DdGGOSU0y9DBJLRGYBLYACIhKIM0jMSCAL8JOrO8paVX1YRIoBn6tqR6AwsMC13gf4SlWXJldcJn0Y7GpLthY/k1xUdauIVMG5XWYnzsjog1T1eAI2Lw4cclsOBBpEq1MRQER+B7yBMQk7dylkLZqAEExmpaqsXLmSf/75h/79+5MzZ05Ph2QSIKEDuezBGcggQnagEPBESgRljDHJTVXvjqH4i1jqHgE6ul7vB2qmYGgmHdi929MRmIxGRLK6Wt/eTaFD+AAVcC52lQBWiUj1WOYijRyEqmqZHOBlw+yb2KkqoaGhDBw4kBw5cng6HJNACW3pezXa8kVgi+vLkDHGpDgRKQiMA24HoowyoKrenojJGGOS4LiIzAG+UNW1N7ntYaCk23IJV5m7QGCdqoYA/4rIbpwkcH30nblGJJ4EEFC5oEa9zm+M49q1ayxZsoSWLVvSpk0bT4djblKCOm2r6rRoj/mW8BljUtl4nC5NDwCXgS7AH8CTHozJGGMSqwvOPcnLRWSHiDwrIoUTuO16oIKIlBURP6APsChanW9xWvkQkQI43T0T8N1NIWuxBIZhMovLly8zbdo0RMRa99KpWFv6RKRxQnagqn8kXzjGGBOrVkB11wia4aq6WET+AeYBH3o4NmOMuSmquhJYKSJDcQbGux94RUSWqmr0QVmibxvq2u5HnPv1JqvqNhF5Gdigqotc69qKyHYgDBiuqqfjDSw8BLyyJOWtmQxGVZk5cybly5enZcuWNiVDOhVX987VCdhecU42xhiT0nyBk67XQSKSXVUPisitngzKZA61ank6ApNRqeol4AsR2YRzO02nBG63BFgSrewlt9cKDHM9bjaqm9/EZEhnz54lT5489OnTh1y5bCqP9CzWpE9VbbxeY0xashuoA2wE/gJGich5ICEj3RmTJOPGeToCkxGJSH6gL9Afp/vlAqCdJ2MCBb/8ng3BpAl79uzh22+/pX///hQsWDD+DUyalpQpG4wxJjWNwpleIeL1bCAnrhHnjDEmPRGRBTjTX20GJgKzVfW8Z6MCVMHbRu/M7DZv3szPP/9Mnz59LOHLIBKc9IlIG66PmhfZmVdVB6ZAXMYYE4WqrnB7vQnXHFTGpIa+fZ3nGTM8G4fJUPYAo1R1h6cDiUpBfD0dhPEgVeXEiRP079+fAgUKeDock0wS1IVTRJ4AFgLlgHtwrq73wloKjTGpTERyikgx94enYzIZX2Cg8zAmuajqs2kv4QM0HLxtIJfMKDw8nGXLlnHmzBnatWtnCV8Gk9CkbSjQUVVXishZVe0pIncA3VMwNmOMiSQiTYApOBefIouxAaWMMemEiIxX1cddryfFVk9VPddtXcPBN4/HDm88IyQkhG+++YaQkBCbkiGDSmjSV8Q1tDBcH9JpCTANZ84sY4xJaZ8BXwMzgCsejsUYYxLDN5bXaUg4ePt7OgiTyr7++muyZs1Kz5498fa266gZUUKTvhMiUlhVjwOBItIAOEUCu4caY0wyKA684BqG3Bhj0h1VHeL2eoAnY4lVeIglfZnIpUuXyJ49O+3btydv3rw2B18GltCkbTbOIC4AnwO/4Iw29VVKBGWMMTH4CQjwdBAmc2rUyHkYk1xEZGks5YtTO5Yb+Ob0dAQmFRw9epRJkyZx6NAh8uXLZwlfBhdnS5+IjAcmqurzEWWqOl5ENgC5gB9TOD5jjIkwGFgiIuuBo+4rVPV1z4RkMos33vB0BCYDahxLecNUjSImXjaQS0a3b98+5s+fT6dOnShVqpSnwzGpIL7undWBv0VkHc4cMnNV9aqq/pHyoRljTBQjgFo4g7e439OngCV9xph0QUTucb30EZG7cZsGC6gAnE39qNyIOA+Toe3du5fevXtbwpeJxJn0qWpLEamAc4X9HWCciMzAaf1LtWGGj68Zn1qHMqngYlCop0Mwycw/Z6rM3vIQUE9V/0mNgxnjrkcP5/mbbzwbh8kQXnM9ZyHqBatw4BjwWKpH5E5sNq6MSlVZs2YN5cqVo127dp4Ox6SyeO/pU9U9qjocKAE8AlQFtorIbyLSN6UDNMYYlwtA2pvTymQKp087D2OSSlXLqmpZYHHEa9ejnKo2UdUfPByhZw9vUkR4eDhLlizh77//JmvWrJ4Ox3hAgkffVNUQVZ2jqq2BukAxnCkbjDEmNbwLjPJ0EMYYkxxUtYunY4iR+Hk6ApMCvv/+e06dOkX//v3JlSuXp8MxHnBTbfgiUg+nq2dv4DTwYkoEZYwxMXgUKC0iw4AT7itUtWJ8G4vIZKATcEJVq7nK8gFzgDLAAaCXqt5wP42I3A+84Fp8VVXtgpcx5qali8nZ7X6+DCU4OBg/Pz8aN25Mnjx58PGx7ruZVbw/eRHJBfQDHgQqA4uBXsCPNl+WMSYVvZrE7acCHwHT3cpGAD+r6psiMsK1/Jz7Rq7EcDTOdBEKbBSRRTElh8YYE490MDm7JX0Zxfnz55kxYwatWrWicuXKng7HeFh8UzZMA3rgTMT+OfCFqh6NaxtjjEluIuKDc7Gph6oGJ2YfqrpKRMpEK74TaOF6PQ1YSbSkD2gH/KSqZ1yx/AS0B2YlJg6TPt1+e/x1jIlPupic3WQIx44dY9asWTRq1MgSPgPE39KXG+gJLLVWPWOMp6hqqIjUBZJ76NfCbheyjgGFY6hTHDjkthzoKruBiAzG6QJPqdy5kzFM42kv2s0MJpmJSG7gmqoGiYgXcB8QqqozPByYRw9vksfWrVtp27YtVatW9XQoJo2Ib8qGrqkUhzHGxOdLYCgwLiV2rqoqIkm6uKWqk4BJAAHFitmFMmNMXBYDw4A/gTHAICBURKqoqg1aZRLln3/+IV++fLRu3drToZg0JsGjdxpjjIfVAd4SkT0islxElkU8krDP4yJSFMD1fCKGOoeBkm7LJVxlJhPp0MF5GJOMKgMbXa/vBdoATQDPTodlHbvSJVXl999/Z/ny5fj6ptHbRY1H2RA+xpj0YpXrkZwWAfcDb7qeF8ZQ50fgdRHJ61puC4xM5jhMGhcU5OkITAbkraphIlIa8FPVbQBu5xrP8LKvhunRqlWr2L59Ow888IBNyWBiZH/Zxph0QVXHJmV7EZmFM2hLAREJxBmR801grog8APyHM1gMIhIAPKyqg1T1jIi8Aqx37erliEFdjDEmCf4RkReAUsAyiOxxcNGjUdnonelKaGgoIkKVKlVo0KAB/v7+ng7JpFE3O0+fAEVsBE9jjCeISEngHpzuloeAmaoamJBtVfXuWFbdMC6jqm7Aub8mYnkyMPmmAzbGmNg9BkwArgH9XWVtcCWAnmNJX3oRHBzM7NmzqVatGgEBAZ4Ox6RxCbqnT0RyiMgXQBCw11XWVURGp2RwxhgTQUSaAjtwplnIDXQBdorIbR4NzBhjEkFVt6hqE1Vtqar/ucqmq2p/D4dm0oHz588zefJkihQpQt26dT0djkkHEtrS9y7OUOZNgOWusvXA60CSulwZY0wCvQU87mp1A0BEBgBvAw09FpXJFDp18nQEJiNKSu+FFAzKo4c3CfPXX39Rq1YtGjVqhNjPzCRAQpO+TkAVVT0fMaS5qh4WkWIpF5oxxkRRGZgarWw68F7qh2Iym2ee8XQEJqNx9V5YCvwN7ANqAy+KSAdV/c2DkXnu0CZeBw4cwMvLi2bNmnk6FJPOJHTKBi+crp2RRCQHcCnZIzLGmJgdx5m2wV0dYp5mwRhj0rqI3guNVbWfqjbBuc/vbc+GZUlfWrVt2za+/vprwsLCPB2KSYcSmvSt5sYhyh8DfknecIwxJlYfAEtE5BURGSgiLwPfu8qNSVEtWjgPY5JRbL0XKqV+KG7E26OHNzHbvHkzy5Yto1+/fpQtW9bT4Zh0KKHdO4cBK0SkL5BDRP4B/IBWKRaZMca4UdVPROQczih3PXDuf3lSVWd5Mi5jjEmkiN4LG9zKPN97wRr60hRVJTw8nJIlSzJgwADy5Mnj6ZBMOpWgpE9VD4lINaAzUAZnPqvvVdWmqzXGpBpXgmdJnjEmI4jovfAp8C/O96uH8PgAeZb1pRWhoaEsXLiQAgUK0Lx5c0+HY9K5BM/Tp6pXgXkpGIsxxsRJRPyBCkBO93JV/cMzERljTOJY7wUTl+DgYObOnUuWLFlo3Lixp8MxGUCCkj4RmRTbOlUdnHzhGGNMzESkCzANZ44+dwrYTSjGmHRDRMoD1YF1aS/Js5a+tODvv/+mQIECtG/fHi+vhA7BYUzsEtrS5xttuRjQDGv5M8aknndxuj1NUtUrng7GZC69enk6ApNRiEh3YA7OxaprItJdVZd4OCyTRpw8eZLLly9Tr149AJuDzySbhN7TNyB6mYh0A9ome0TGGBOzwqo6ztNBmMzpkUc8HYHJQF4ARgEfA0Ndr9NQ0mdJhqccPHiQuXPn0rZtW0v2TLJLSnvxt0DvZIrDGGPis0xEGng6CJM5XbniPIxJBmWBd1X1MvAeUD6xOxKR9iKyS0T2isiIOOr1EBEVkYD4d5rYaExS7Nmzhzlz5tCtWzdq1Kjh6XBMBpTggVxi0IFoE7YbY0wKOgB8JyJzgKPuK1T1dY9EZDKNjh2d55UrPRqGyRi8VTUcQFVDRMQvMTsREW9gAtAGCATWi8giVd0erV5O4AlgXQL3nJhwTBKEh4eTP39++vbtS9GiRT0djsmgEjqQyx6cwRIiZAcK4ZxEjDEmNdQFtgHVXI8ICljSZ4xJL/xEZJTbsn+05YReyKoP7FXV/QAiMhu4E9gerd4rwP+A4QkLT+OvYpKFqvLzzz8TEhJChw4dPB2OyeAS2tL3arTli8CWiBONMcakNFVt6ekYjDEmGazFaZ2LsC7ackIvZBXHmeYhQiAQpQu8iNQBSqrqYhGJNekTkcHAYIBbS+dNwKFNUoWFhbFo0SLOnDnD3Xff7elwTCYQb9InIj5AYWC8qganfEjGGGOMMRmTqrZIjeOIiBfOPYP946urqpOASQAB1UtbU18q2L59O1evXuW+++7D1zf6IPnGJL94B3JR1VBglCV8xpjUJiK/ikjzeOo0F5GVSThGJRHZ4va4ICJPRqvTQkTOu9V5KbHHM8aYZHIYKOm2XMJVFiEnTlf4lSJyAGgILErQYC4mxVy8eJF///2XatWq0atXL0v4TKpJaPfOX0Skuar+mqLRGGNMVK8DH4uIL7Ac516VC0AuoApwOxAKDEvsAVR1F1ALIgdGOAwsiKHqb6raKbHHMelb//6ejsCYG6wHKohIWZzzVh/gnoiVqnoeKBCx7Lo49oyqbkjlOI3LqVOnmDlzJnXr1qVs2bI2LYNJVQlN+g4AC0Vknut1eMQKGzXPGJNSVPVHoKqItMMZoKAvkBc4C2wGHnfVSS63A/tU9b9k3KfJACzpM2mNqoaKyFDgR5yJ3ier6jYReRnYoKqLErdnS0RSwuHDh5k1axa33347tWvX9nQ4JhOKM+kTkQuqmgvnKvhmoJzrEcFGzTPGpDhXYpecyV1s+gCzYlnXSET+Ao7gXC3flgrxmDTi1CnnuUCBuOsZk5pUdQnRJnZX1Ri7n6fWvYTmRqpKjhw56NatG+XKlYt/A2NSQHwtfQI2ap4xJuNzzZXVBRgZw+pNQGlVvSQiHYFvgQox7CNyBLxSuXOnXLAm1d11l/Ns8/SZ5CZOH78iqno03som3dmwYQOBgYF07dqV3PZ/wXhQfAO52AhOxpjMogOwSVWPR1+hqhdU9ZLr9RLAV0RuaPNR1UmqGqCqAQWzZUv5iI0x6ZaI5BCRL4AgYK+rrKuIjPZwZJ49fAahqqxYsYI1a9bQrFkzT4djTLwtff4iMjmuCqo6MBnjMcYYT7mbWLp2ikgR4LiqqojUx7lgdjo1gzPGZDjv4kyJ1QRnoCpwBmd5HRjrqaBM8ti/fz/79+9n4MCBZM+e3dPhGJOggVzCUjwKY4zxIBHJjjM58kNuZQ8DqOpE4C5giIiE4lyV76Oq1hPCGJMUnYAqqnpeRBRAVQ+LSDEPx2WS4Nq1axw5coRy5cpRpkwZvL29PR2SMUD8SV+wqj6YKpEYY8xNcA1THqaqB5O6L1W9DOSPVjbR7fVHwEdJPY4xxrjxwrmIFElEcgCXPBNOZBSePXw6dunSJb766iuKFy9uCZ9Jc+KdnN0YY9ICEZkiIk1cr+/GuQdmv4jcE/eWxiTdkCHOw5hktJobB456DPjFA7FcZzlfopw5c4bJkydTsWJFOnbs6OlwjLlBgkbvNMaYNKA98Ijr9TCgB3Ae+BD4ylNBmcyhd29PR2AyoGHAChHpC+QQkX8AP6CVZ8MyN0tV8fX1pXnz5tSsWdPT4RgToziTPlXNmVqBGGNMPLKpapCI5MWZL3Sha2CVkp4OzGR8hw45zyXtt80kE1U9JCLVgM5AGeA/4HtVDYpzwxRn1/tvxu7du/nrr7/o2bOnJXwmTUvIQC7GGJMWHBaR5kBl4DdXwpcLCPVwXCYT6NfPebZ5+kxyUtWrwDxPx2ESZ9OmTfzyyy/0tq4AJh2wpC8FdelwO0ePHLmhvMltzRj30acAfD3nK2ZMncypUye5pVx5hj07ktp1AiLrvjr2RTb8uY5TJ0+QNVs2atSszdAnhlH2lnKp9j4yqy2bNjDryyns2rmdUydPMGr0q3Ts3C1y/WefjOeX5cs4cfwYPr6+VLq1MoMefozqNWtH1jl96iQff/Au6//8g8uXLlOiVCnuve8B2nboFFln2hefsvb3VezZvYvg4CBWb9iWqu8zHXkZ+Am4BkTcMNEa2OKpgIwxJrFEZFJs61R1cGrGYm7e0aNHWb16Nf379yd//vzxb2CMh1nSl4KmzfyasPDrM16cPnmSfnffReu2HQBYtnQJ7771Bs+NepFatesyb84snnjkIeYu+I4iRZ0Rm6tUqcYdne+kcOGiXLhwjkmfTODRwQNZ9MNyfHx9PfK+MougK1e4pXwF2t/RhVdHj7phfanSZRn23AsULVacq1evMver6Tz9+EPMnr+EfPmdebtfHT2KCxfO88a7H5EnT15WrfyZV14aQaHCRajlSu5DQq7RrFUbatWtz5dTYv0OkOmp6mwRWeh6HdH9aTXwh+eiMsaYRIv+T7wY0Axr+UvTwsPDOXz4MCVLlmTIkCH42ncxk07Y6J0pKG++fBQoUDDy8fvqVWTPkYPWbdsD8NWX0+jUpSvdevSi7C3lGD7yBQoULMC8ubMj99G9Z29q1wmgWPHi3Fq5KkOGPsHJkycIPBzoqbeVaTRq2oyHHn2Slq3b4eV14z0O7Tp2JqB+Q4qXKMkt5crz2FPPcuXyZfbs3hlZZ+vfm+ne826qVqtB8RIlubtvfwoVLsL2bf9E1hn08GPc3bc/FSvdmirvK527BtQWkYi+NJdwBnMxxph0RVUHRHu0A+7BpmxIs65du8acOXNYvXp15OAtxqQXlvSlElVl4YJv6NCxM/7+/oSEXGPnjm00bNQkSr0GjZrw91+bY9xH0JUrfLdwPkWKFqVYseKpEbZJoJCQayxc8DXZs+egQsXryVv1WnVYsXwp58+dIzw8nN9WruDc2bPUq9/Qg9GmTyJSDtgKLAG+cBW3BT7zWFDGGJO8vgU8fINYuGcPn0ZdvnyZ6dOnkzVrVnr16oWIJccmfbHunalk3Zo/OHI4kK49egJw7uw5wsLCyBetH3i+fPn589SaKGVfz/mKD99/l6CgK5QuU5aPJ03Bz88v1WI3sfv9t5WMGfUMwcHB5C9QkPcnfBbZtRPglTfeY/SoZ7ijdRO8vX3w8/NlzGtvU6FSZc8FnX59CMwGXgFOu8pWAh94KiCTeTz9tKcjMJlEB6JN2J76bELxmIgIVatWpWHDhpbwmXTJkr5U8u38r6lStXqiuvB16NiZBg0bc+rUSWZMm8KIZ57ii2kz8c+aNQUiNTejTkB9pnz1DefOneO7BfN4aeTTTJzyFQUKFAScwV7OnzvLuI+/IHeePPy2cgWvjh7JR59Ni9IiaBKkPtBFVcNFRAFU9ZyI5PFsWCYz6NzZ0xGYjEZE9gDqVpQdKAQ84ZmITEyOHDnCb7/9Rq9evWjUqJGnwzEm0SzpSwVnTp/m119W8OyoFyLL8uTNg7e3N2dOn45a98xp8hcoEKUsR86c5MiZk1Kly1C9Rk1aNW3IiuXL6Nj5zlSJ38Qua9ZslChZmhIlS1Otek36dOvA99/Oo/+gIRwOPMi8OTOZ8tU3kQlehYq38teWjXwz5ytGvPiyh6NPdy4AeYBTEQUiUgw47qmATOaxa5fzXKmSZ+MwGcqr0ZYvAltUdb8ngolkjViR9uzZw7fffkvnzp2tdc+ke5b0pYLvFy3Az8+Xdh3uiCzz9fXj1spVWbf2j8iBXQD+XPMHLVu3jXVfqqAo10KupWjMJnHCw5Vr15yfTXBwMADeXlG7ynh7eREebvdMJMJ8YLKIPAIgIvmBcThdPo1JUQ895DzbPH0mOYiID1AYGK+qwZ6Ox9zo3LlzLFq0iD59+lCyZElPh2NMklnSl8JUlYXz59GmfUeyZcseZd09/e5n9PMjqFqtOjVr1eGbr+dw8uRJevR07uE+dPA/VixfRv2GjcmbNy/Hjx9n2uTP8PP1o2mzFh54N5nLlSuXOXzoIOAkc8ePHWXPrh3kzJ2bnDlyMXP6FzRp1pL8BQpw7uxZ5s+dxckTx2jVxkniS5cpS4mSpXj3f6/w6BPPkDtPHlatXMH6dWt4490PI49z7NgRLp4/z9GjzpyOe3btAKB4yVI3/M5kci8CnwMHXcsngK+A1z0WkTHGJIKqhorIKFV9y9OxmKhUlWPHjlG0aFEeffRR/P39PR2SMcnCkr4UtnH9nxw8+B8vv37jeb1t+46cP3+OyZ9N5NTJk5QrX4FxEyZS1DUyp6+fHxs3rGfm9KlcvHiRfPnzU7tuAJO/nBV5z5hJOTu3b+PxhwdELn/x6QS++HQCHTrdydMjXuTf/ftYvGgBF86fI1fuPFSuUo2PPptO+QpO/y8fH1/e/mAiEz98j+eGDSXoyhWKlyzJqNGv0rRZy+v7nfgRP3y/MHJ5wL13ATB+4hTqBNRPpXeb9rnm5rtXRJ4AygD/qepJz0ZljDGJ9ouINFfVXz0diHGEh4ezZMkSjhw5wqBBgyzhMxmKqGr8tTzsQnB42g/SJNjVEOvamNEUzOljNztEE1CsmG4YPDj+imPGpHgsJulatHCerXtnxiEiG1U1wIPHfx8YgDMZ+wHc5kpQVY/1YAioWUE3/LXHU4f3mJCQEL755htCQkLo1asXWbJk8XRIxtwgKecta+kzxqRZIvITUUe3i5Gqxn4jrDHGpCEickFVcwG1gM1AOdcjgmLd1lNdWFgYhQoVonnz5nh727QVJuOxpM8Yk5at9nQAxgC88EL8dYxJIAFQ1ZbxVTQp79y5cyxfvpyuXbvSqlUrT4djTIrxaNInIgNUdUos6wYDgwHGffQJAx5IQDcpY0yGoqpjU+M4InIAZ7j0MCA0etcJccbq/gDoCFwB+qvqptSIzaQNrVt7OgKTgdgtK2nE0aNHmTVrFk2bNsXHx9pBTMbm6d/wsUCMSZ+qTgImgd3TZ4xxiEgOoBNQAggEFqvqxWTafUtVPRXLug5ABdejAfCJ69lkElu2OM+1ankyCpNB+IvI5LgqqOrA1Aoms7py5QpfffUVHTp0oEqVKp4Ox5gU55XSBxCRv2N5/IMzR02q2rj+T+rVrMy5s2dT+9BpzpgXR/LU0Ic9HYZx2bThT5oGVOXcubh/N4cO7s97/4s+p2/GJyIBwH7gf8Cdrud9rvKUdicwXR1rgTwiUjQVjmvSiCefdB7GJJOweB4mBZ06dYps2bLx0EMPWcJnMo3UaOkrDLQDon+TFeCPxOxwzIsjWbzo2xvKZ8yZT6VbKydml8nqoQfuY9OG9Yx99U06dr4zsvy7hQt4+41XWbV2Y6rGs3H9nzw86H5+WvkHefLmjSx/5tlRpPXRW5sGVI1zfYdOd/L8mNS53/3okcP07HJ9vJAcOXNRrnwFBj38GLXr1kvy/qvXrMXCpSvJnTsPAEu+W8D7b73GT79tiFLv9bfH4ePjm+TjpUMfA++q6v8iCkTkWZxWt6T+ABRYJiIKfOrqaeCuOHDIbTnQVXbUvZJ7t/RSuXMnMSRjTAYVrKoPejqI2GXcwZhVld9//52NGzcyZMgQcuTI4emQjEk1qZH0fQ/kUNUt0VeIyMrE7rR+w0aMfe1/Ucry5MkbS+3UlyVLFiZ+PJ7W7Trg5+fn6XBilCNnTk+HEK+FS1dGvv5j9a/879XRUcqyRJtDJzQ0JMUTonc//JTyFSpx9uwZJn38AcOfGML0Od9SrHiJJO3X19eP/AmYfzGXKynMhCoD70Yrew9n0vakaqqqh0WkEPCTiOxU1VU3uxP3bukBxYql7SsqxmRgY1aO8XQIJo0JDw9n6dKlHDx4kAEDBqTZ72bGpJQU796pqg+oaowj8KnqPYndr5+vHwUKFIzy8PHxYeb0qdx9153c1qAOHVs359UxL3DxwoVY93Pp4kVeGvUsbVs0oUm9mtzZsQ1fzZgWZf1rL79E2xZNaN6oLoMH9mP7tq3xxte6XQeuBl/l69lfxVnvry2bGTywH00b1KZj6+a8+eoYLl26FLk+6MoVRj//HM0a1qVdy6ZM+WISTw19mDEvjoyss+T7Rdx3T0+aN6pL2xZNGPHMk5w4fhyAI4cP8/Cg+wFo06Ix9WpWjtzWvXvn/HlzadeyKWFhUXuVvDDiGYY9/kjk8qqVv9CvTw/ns+rQmo8/HEdIyLXI9SuWL+Puu+6kaf1a3H5bQwYP7Mfp07HdJhW//AUKRj5y5MgZpezatWu0b9GQn5Yu5vGHB9CqSR0WfjOXJd8toM1tUXv8xdR18p+/NjN08P3c3qQuXTu05J03Xuay22cfm9y585C/QEHKV6jE8JGjCQ4O4s+1TqP1lk0bePD+PrRqXJvObZsx/t03o3w+WzZtYHD/u2lzWwDtmjfgwft6s3/vnhti3LThT14f+wJBQUE0DahK04CqfPHpBCBq985PJ4xjYN+eN8T48MB7Gff29RbQxYsW0LdnZ1o1rk2f7h2ZM3Ma4eHX50v89pu59OnekVaNa3PH7U0YNvRBQkND4/0sUtkWoFq0suqu8iRR1cOu5xPAAqB+tCqHgZJuyyVcZcYYc7MyblNaGhbxP61///7kypXLw9EYk/pSPOlLbeIlDBs+kjnzv+OVN99m29Z/ePvN2O9/+uSjD9i7dw/vf/gJ8xYu4aWxr1GokHOroary5NCHOXniOO9/+Akz5syndp0AHnmwP6dOnogzjmxZszHo4UeY8vnEWJPOvXt289jDg2jWvBVfzV3AW++NZ/eunbwy+vnIOuPefYtNG9fz9vvj+fizKezZtZPNm6N2Dw0NCeGhIUOZ+fW3vP/hJ5w7e5YXRjwNQOEiRfjfux8AMGf+d/zw8yqeeXbUDbG0btuOS5cusm7t9R63V65c5tdfVtDhjs4ArPl9NS+NGk7PPvcyZ/53vDj2NX7+6UcmjB8HwKlTJ3n+uWe4o3NX5i5YzKeTp9OxU5c4P6fk8OmEcXS7qw8z5i7itha3J2ibfXt3M2zogzRp1pKps+bz2tsfsGf3Tt54+ebGZY+YvDUsNISTJ47zzOMPU7FSZSbPnMeIF19m+bIlTPxoHOD8wxnx9GPUqFWHqbPmM2naLHre0w8v7xv/DKvXrMXjT4/A3z8rC5euZOHSldzdr/8N9dp26MTundv578D+yLLDgYfY+vcW2nboBMCiBV8zacI4HnhoKDO+/o6hTw5n5vQvWPD1LAB2bt/K+2+9ysAHH+GrbxYz7pMvaNCo6U19DqlkGfC9iIwRkQEiMhZYhNMt856Ix83uVESyi0jOiNdAWyD6lZ1FwH3iaAicV9WjGGPMTVLVtN/NJgMJCgpi4cKFhIeH07FjR/yj9RAyJrPw9Oidibbmj9U0a1g3crlWnbqM/3gS9/S9P7KsWPHiPPbUMzzzxKOMefVNvLxu/HJ99OgRbr21ClWr1wCgaLHikes2/LmO3bt2smzl75EniSFDn+C3VSud1rUBg+KMsVuPXsyeMZ2pkz/jsSefvmH9l1O/oE279vS9f0Bk2XPPj6Zv7+6cOX0a/6z+LPp2PmNffYMGjZoA8OKYV7mjbdSpfbp06xH5ukSJkox4YTQ9u97B8ePHKFy4SOQ9Yvny5Y9yT5+7XLly07hpM5Yu/o7GTW4DYOWKn/H28aZZC2femimfT6Tv/QPp0rW7c6ySpXjsyad5adRzPDFsOKdOniA0NITb27SN/BzLV6gY52eUHHr0vpeWrdvd1DZfTZ9CqzYduLtv/8iyZ0a8yIB77+LsmdPkzZc/3n0EBV3h0wkf4O3tTa269Zj/9WwKFCzI0yNexMvLizJly/Hw0Kd4+/WxPDjkMa5dvcqlixdoclsLipcoBUDpMrfEuG9fXz9y5MiBCHF2+Sx7S3kqVqrMsh++58EhjwPw09LFlCxVhirVnN/pqZ9PZMjjT0d+RsWKl+BI4CEWzJtNj973cvzYUfz9s9K0WUuyZc9OkaLFqFDx1gR9jqlsIBAC3O9WFuoqj6BA3M3rNyoMLHBmZcAH+EpVl4rIwwCqOhFYgjNdw16cKRsGxLIvk0G9blNlG5PunD9/nhkzZlC+fPnIi7TGZFbpNumrXSeAUS9dn8IrSxYnKVu/bi1TJ0/iwP79XLp0kbDwcEJCQjh96hQFCxW6YT89evVhxNNPsnPHNuo3bMxtzVtQN8Dp2bVzxzaCg4No26JJlG2uXbtK4KFDN+wrOh8fH4Y89iRjXhxJ77v73rB+x/ZtBB46yE8/Lo0sixhYJTDwIP7+WQkNDaGKKyEFyJotG+XKV4iyn507tvHZxI/ZvWsHF86fJ2JslmNHj1K4cJF444zQ4Y7OjH1xJMFBQfhnzcrSJd/T6va2kSfKHdu3s23rP0yf8kXkNuEaztXgYE6fOkmFirdSv2Ej+vToQoNGTajfsBG3t25H3nz5EhxDYtxaOe7BXmKya+c2Dh86yIqffogsi/jcDgceijPpe/TB+/HyEoKDg8lfoCCjRr9GufIV+eLTCVStVjPKxYUateoQEhJC4KGDlK9QiY6du/L0Y4OpW68hdes1oEXrthQpUuym43fXtkMn5n89O0rS17bDHQCcPXuGE8eP8fbrY3n3zZcjtwkLC4v8XavXoDFFihajZ5e21G/UhPoNG9O8ZRuyZc+epLiSm6qWTaH97gdqxlA+0e21Ao+mxPFN+tC4sacjMMbcjJCQEKZOnUr9+vVp1KiRp8MxxuPSbdLn7+9PyVKlo5QdPXKYpx57mK7de/LQI4+RO3cedu3YzvMjniEkJCTG/TRp2ozvlv7MH6tX8eefa3lq6BBub9OO0a+8Tnh4OPny5+ezKTNu2C579oSN+NS6bXtmTJvCpx+Pp1adqPeZqSp3dr8rSutkhIKFCnPwvwPx7j/oyhUeG/Ig9Rs4A9vky5efc2fP8uCAvoTG8p5j07RZC7y9ffh15Qrq1W/In+vW8OHHn7nFG86ghx+ldZsbW9Xy5M2Ht7c3H038gn/+3sK6NX+waME3TBj/Pp9+MZ2KlVKu5Shr1qxRlkW8iD4oaVi0+9M0XOnUtQe977nvhv0VLBj3TCKjX3uLcuUrkCNHLnLnyZOgGF2tSIwa/Ro97+7Huj9Ws3rVSiZ9Mp433hmfpO6UrdvfwScfvsfWv7fg6+fHfwf207aD0yVXXfftPTPyJarXrBXj9tmyZ+eLGV/z1+YNrF+3hi+nfM6nEz7g8+lzKFDwxgslxmRGf7h6vlvyZ0zad/78eXLnzs39999PngT+nzYmo0u3SV9MdmzfRkhICE8NH4G3tzcAq1f9Gu92efLmpWPnO+nY+U4aN7mNF0Y8w8gXx3Br5SqcOX0a8fKiRImS8e4nNo899TSPDh54w6iLlSpXYf++vTckrxFKlCyJj48v27f+E3n84KAg9u3dQ/GSzvKBA/9y7uxZHnnsKYqXcEaPXLFvWZT9+Pg6o1mGhcc99Y+fnx+3t23H0sXfce7sWfLnL0DdetfHs6hUuQr//bs/1njBSW5q1KxNjZq1GfTQI/Tu3pmffvwhRZO+6PLkzUdwcBCXL10iu2s45j27d0apU/HWyvy7bx8lSsb+XmJTqFDhyO6Z7kqXuYVfli8lPDw8srXv7y2b8PX1pbjb70+FirdSoeKt9O0/iKcff4gfvl8YY9Ln4+tLmNtgK7EpUKAgdQIasOyH7/H186NajVqRx8uXvwAFChbiyOFDdOh0Z6z78PHxcbU+NuSBhx6lc5tm/P7bSu7s3ive46cWESkCjAUCgCj3xKhqyvcjNpnaKNet0CtXejQMYyKJSHvgA8Ab+FxV34y2fhgwCKcb/ElgoKr+l+qBprKtW7eydOlShgwZYgmfMW4yVNJXslRpwsPDmTVjOi1vb8PWf/5i1szpcW4zccJ4bq1chVvKlScsLIxffv6J4iVK4ufnR/2GjalZqw7PPPEojz31DGXK3MLp0ydZ8/tq6jdsRO06CZsTum5AfRo1bsrXs2fi5eUdWX7/gEEM6NeHN14ZQ/e7epEte3YO/Luf335dyaiXxpItW3a6dO3ORx+8S568eSlQoCCTP5tIuIYjrsG/ihQpip+fH3Nnz6Rnn3s4sH8fEyeMj3L8okWLISL8vupXbmvekiz+WciWLeauex3u6Mwjgwdy+HAg7Tp0jNJVcdDgR3jq8SEUKVqM1m3b4+Pjw769e9i29W8ef2o4//y9hT/XrqFh46bkz5+fXTt3cPzYUcreUi5Bn1NyqVqtOlmzZmXihPfpfc997N29iwXzZkepc+/9D/BQ/3t4+/Wx3Nm9F9myZ+O/A//y+6qVPPv8mEQdt3vPPnw960veffMVet7dlyOHA5n40ft073UP/v5ZOXI4kIXz59K0WUsKFirMkcOH2LdnN1179I5xf0WLFufa1ausX/sHFW6tjL+/P/7+WWOs27ZDJyaMexsfX1/uGzg4yroHHnqU999+nRw5ctKoSTNCQ0PZtXM7p06eoN+AB/n9t5UcDjxErdoB5Mqdm00b1nHlymXKlE3dn1sCzHQ9f45zX50xxmRKIuINTADa4Mwbul5EFqnqdrdqm4EAVb0iIkOAt4CY/+FkAKrKmjVrWLduHf369SN7GrtFwRhPy1BJX4WKlXj62VFMn/I5Eyd8QPWatXli2HBGPTss1m38/Pz4+MNxHDlymCx+WahWowbvjf8YcFqtxk34lE8++oDXx77EmTNnyJc/PzVr1eaOzrG3msTk0SeGcW+vbmTJcj3pq1CxEpMmf8knH33AQw/cR1hYOMVLlKBFq9aRdZ54ejhBQVd4+vFHyZYtG3f3vZ/Tp0+TJYszv0zefPkY/cobfPzhOObN+YryFSrx1DMjePyR6/O+FipcmMFDhvLxR+N4deyLdOx8J2NeeSPGOGvXCaBQoUL8u38fr/0v6pRojZo0ZdyHE/li0ifMmD4FH29vSpUuQ6cu3QDIkSMnf23ZzNxZM7l48QKFixTlgcFDUmUET3e5cufhpVfeYsL4d1i8aAG1atdl0MOP8cpLIyLrlK9QiQmfTWPSJ+MZ+tD9hIeFU6x4CZq1TNjonzEpWKgw74yfyIQP3mHAPT3IkTMXbdp15KFHnwScLsmHDv7HiyOGcf7cWfLmy0/b9nfQt/8DMe6ves3adO3RmzHPD+f8+XMMePARHngo5tvKmrdqzbtvvkLopYvc3qZDlHWdu96Fv39WZn05hU8njMMviz9lbylHj17OQJc5cubkt5U/M/XzTwgODqZ48ZI898LL1KxdN6ZDeVIAUFhVgz0diDHGeFh9YK/rnmREZDZwJxCZ9KnqL2711wI3Di6QgYSHh3Py5EkGDhxI7ty5PR2OMWmOaPSbn9KgC8HhaT/IVHLt2jU6t7+dfvcPjDLqZ3pyNST+LosmfSmY0yfF550SkT+BO9PLVAkBxYrphsGD4684ZkyKx2KSrkUL59m6d6YPCZmcfWzLsRtVNWFddtIYEbkLaK+qg1zL/YAGqjo0lvofAcdUNcY5rERkMDAYoHzpQnX3HDieMoGngNDQUH7++WeaNm1qrXsmwxORRJ+3MlRLX0a0a8d2/v13P1WrVefK5ctMm/I5Vy5fpk37DvFvbEzG8iDwiYhMB465r1DVP2LexBhjMjcR6YvTU6J5bHVUdRIwCSCgZsV0c6E9ODiY2bNnkz17dpuSwZh4WNKXDnz15VT+O/Av3t4+VKx0K5OmfHlTUzEYk0FUBm4HovcXVpyBDIxJMePGeToCY6I4DLiPMFfCVRaFiLQGngeaq+rVhO06xTtuJIvw8HCmT59OyZIladeuXYxzMRtjrrOkL42rVLkK02fN83QYxqQFbwPPANNVNcjTwZjMpVYtT0dgTBTrgQoiUhYn2esD3ONeQURqA5/idAM9kfohppzLly+TPXt2unXrRoECBSKnRTLGxM6SPmNMepFDVT/1dBAmc1q+3Hlu3TruesakBlUNFZGhwI84PR0mq+o2EXkZ2KCqi3AulOUAvnYlRQdVNXVHVksBBw4cYN68eQwcOJCCBQsm235DQkIIDAwkONjGCjOe5+/vT4kSJfB1TbuWHCzpM8akF/NFpL2qLvV0ICbzedU1/IUlfSatUNUlwJJoZS+5vc5wv63btm1jyZIl9OjRg3z58iXrvgMDA8mZMydlypSxlkPjUarK6dOnCQwMpGzZssm2X0v6jDHphS/wjYisAKKM4KmqCRgm0xhjTHqlquzevZt+/fpRpEjyj2sQHBxsCZ9JE0SE/Pnzc/LkyWTdryV9xpj0IgyY63qdfP0djDHGpFmqym+//UatWrXo1q1bih7LEj6TVqTE76IlfcaYdEFV0+fElMYYYxIlNDSUhQsXcv78eerVq+fpcIxJ12x8W2NMuiIi/iJSUkRKRTw8HZMxxpjkpap8/fXXhIaG0q9fP7JmzerpkFKct7c3tWrVolq1anTu3Jlz585Frtu2bRutWrWiUqVKVKhQgVdeeQXV61Mq/vDDDwQEBFClShVq167N008/7YF3ELfNmzfzwAMPeDqMWF29epXevXtTvnx5GjRowIED/2/vzuOjqLIFjv+OCRoii4CACiIgImEJAQEVBFkEGQZxfyiKBGUUFH2DMy7jNogzjIo7yLihIO4LAipvZEAZUUYxYlAEQdCIgApECImsSc77496ETqeTNNk6nZzv59OfdHXdqjpV6bpdd6lbaSHTPfroo3Ts2JEOHTrwSIjn+Tz44IOICNu3bwcgIyODc845h86dO9OhQweee+45ALZt28bgwYMrancKsUKfMSYqiEhrEVkG/AakAd8HvIypUE8+6V7GmIq3d+9eRIS+ffty8cUXl+sIhlVZ7dq1SU1NZdWqVTRs2JDHH38cgD179jBs2DBuvfVW1q5dy8qVK1m2bBnTp08HYNWqVYwfP54XXniB1atXk5KSQps2bco1tuzs7DKvY/Lkydxwww2Vus1DMWPGDBo0aMD69euZMGECt9xyS6E0q1at4umnn2b58uWsXLmSd955h/Xr1+fP//HHH1m4cCEtWhysj3788cdp3749K1euZMmSJfzpT39i//79NG7cmGOPPZaPP/64UvbPCn3GmGgxDfgR6AxkAonAXKDqVhuaauPkk93LGFOxtm3bxhNPPMHmzZs59thja+xD108//XQ2b94MwEsvvUSvXr0YNGgQAPHx8UybNo17770XgPvvv5/bb7+ddu3aAa7FcNy4cYXWmZWVxejRo+nUqROJiYm8+eabANSpUyc/zRtvvEFycjIAycnJjB07llNPPZWbb76Zli1bFmh9POmkk/jll1/Ytm0bF154Id27d6d79+4hCzGZmZl8+eWXdO7cGYDly5dz+umn06VLF3r27MnatWsBmDlzJsOGDaN///4MGDCA3377jSuvvJIePXrQpUsX5s2bB7hHd/Tu3ZuuXbvStWtXli1bVupjnWfevHmMGjUKgIsuuojFixcXaE0FWLNmDaeeeirx8fHExsZy5plnMmfOnPz5EyZM4P777y9wT56IkJmZiaqSlZVFw4YNiY11d9idd955vPjii2WOPRx2T58xJlqcCrRU1UwRwT+T6hrgP8DMyIZmqru333Z/zzknsnEYU51t3LiR1157jYEDB9KsWbPIBvNSBQzqMkJLTgPk5OSwePHi/K6QX3/9NaecckqBNCeeeCJZWVns2rWLVatWhdWd85577qF+/fp89dVXAOzYsaPEZTZt2sSyZcuIiYkhJyeHt956i9GjR/Ppp59ywgkn0LRpU0aMGMGECRM444wz2LhxI2effTZr1qwpsJ6UlBQ6duyYP92uXTuWLl1KbGwsixYt4rbbbssvhK5YsYIvv/yShg0bctttt9G/f3+effZZdu7cSY8ePTjrrLNo0qQJ//73v4mLi+Pbb7/l0ksvJSUlpVD8vXv3JjMzs9DnDzzwAGcFPYNn8+bNHH/88QDExsZSv3590tPTOfroo/PTdOzYkdtvv5309HRq167NggUL6NatG+AKjc2aNcsv2OYZP348w4YN47jjjiMzM5NXX301vzKjW7du3HHHHSX+H8qDFfqMMdEiF9jj32eJyFHAr0CZ7ukTkeOB54GmgAJPqeqjQWn6AvM42JV0jqpOKst2TXR58EH31wp9xlScFStWcN5555V718RSCbOAVp727NlDUlISmzdvJiEhgYEDB5br+hctWsQrr7ySP92gQYMSl7n44ouJiYkBYPjw4UyaNInRo0fzyiuvMHz48Pz1rl69On+ZXbt2kZWVVaAF8aeffqJx48b50xkZGYwaNYpvv/0WEeHAgQP58wYOHJj/HMaFCxcyf/58HnjgAcB1/d24cSPHHXcc48ePJzU1lZiYGNatWxcy/qVLl5a4j4ciISGBW265hUGDBnHkkUeSlJRETEwMu3fvZvLkySxcuLDQMu+99x5JSUm8//77bNiwgYEDB9K7d2/q1atHkyZN2LJlS7nGWJSa2WZujIlGXwO9/PtPgYeBxyj7PX3ZwJ9UtT1wGnCdiLQPkW6pqib5lxX4jDHVUwQeW7BixQp27txZdQp8EZJ3T98PP/yAqubf09e+fXs+//zzAmm/++476tSpQ7169ejQoUOh+YcisCvi3r17C8w78sgj89+ffvrprF+/nm3btjF37lwuuOACAHJzc/nkk09ITU0lNTWVzZs3Fyjw5e1b4LrvvPNO+vXrx6pVq3j77bcLzAvcpqry5ptv5q9748aNJCQk8PDDD9O0aVNWrlxJSkoK+/fvD7lvvXv3JikpqdBr0aJFhdI2a9aMH3/8EXD3E2ZkZNCoUaNC6a666io+//xzPvzwQxo0aEDbtm3ZsGED33//PZ07d6Zly5Zs2rSJrl278vPPP/Pcc89xwQUXICK0adOGVq1a8c033+Qf78oapMgKfcaYaHEDrmUP4CagOdANuKYsK1XVn1R1hX+fCawBItyvyBhjqj9VZdGiRSxbtqzQvVM1WXx8PI899hgPPvgg2dnZXHbZZXz00Uf5BZU9e/Zwww03cPPNNwNw0003MXny5PzWrtzcXJ544olC6x04cGB+QRIOdu9s2rQpa9asITc3l7feeqvIuESE888/nxtvvJGEhIT8AtGgQYOYOnVqfrrU1NRCyyYkJBQY8CQjIyO/C+/MmTOL3ObZZ5/N1KlT878fX3zxRf7yefd8zp49m5ycnJDLL126NL/AGPgK7toJMGzYMGbNmgW4exv79+8f8nl5W7duBVx35Dlz5jBixAg6derE1q1bSUtLIy0tjebNm7NixQqOOeYYWrRoweLFiwH45ZdfWLt2La1btwZg3bp1Bbq9ViTr3mmMiQqq+mXA+++A8u33AohIS6ALriUx2OkishLYAvxZVb8OsfzVwNUALerXD2+jEyeWTxpjjIky7777Lr/88gtXXnkl8fHxkQ6nSunSpQuJiYm8/PLLjBw5knnz5nH99ddz3XXXkZOTw8iRIxk/fjwAiYmJPPLII1x66aXs3r0bEWHo0KGF1nnHHXdw3XXX0bFjR2JiYvjrX//KBRdcwL333svQoUNp3Lgx3bp1Iysrq8i4hg8fTvfu3QsU1B577DGuu+46EhMTyc7Opk+fPoUKne3atSMjI4PMzEzq1q3LzTffzKhRo/jb3/7G73//+yK3d+edd/LHP/6RxMREcnNzadWqFe+88w7XXnstF154Ic8//zyDBw8u0DpYWldddRUjR46kTZs2NGzYML8r7JYtWxgzZgwLFiwA4MILLyQ9PZ1atWrx+OOPc9RRRxW73jvvvJPk5GQ6deqEqnLffffl3yf4wQcfFLv/5UmioWZl197cqh+kCdu+A7mRDsGUs8Z1YyusP5CIxOLyqgMBnyUDScCHqjqniEUPdTt1cIPC/D14nSJSD8hV1SwRGQI8qqonFbe+bscdpylXX10eoVmhrwro29f9XbIkklGYcE1cMrHENHf3u/tzVe1W8dFEl25J7TQl9ZsK3caBAweIjY1ly5YtNGnSpEo8kmHNmjUkJCREOoxq7eGHH6Zu3bqMGTMm0qFUGX369GHevHkh768M9Z0UkVLnW9bSZ4yp6l4F3gOeAhCRO4C7gC+Ba0TkelV9piwbEJFawJvAi6EKkaq6K+D9AhGZLiJHq+r2smzXRI/ZsyMdgYHwCnOmasvKyuLFF1+kX79+tG3bNtLhmEo0btw4Xn/99UiHUWVs27aNG2+8MawBdcqD3dNnjKnqugHvBExfD4zxNV2XA4UfRnQIxHXYnwGsUdWHikhzjE+HiPTA5Z3pZdmuiS7HH+9expjS2759OzNmzKBdu3acdFKxnSVMNRQXF8fIkSMjHUaV0bhxY84777xK25619BljqroGqroFQEQSgPrAa37eXHwLYBn0AkYCX4lIqv/sNvyjIFT1CeAiYJyIZOMeG3GJRkPfeFNuXn3V/fUjlBtjSuG///0vffr0oUuXLpEOxZgaxwp9xpiq7jcRqaOqWbhWv1Wqmje2s1DGfExVP/LrKS7NNGBaWbZjots//+n+WqHPmEO3bt06GjVqxNChQ0OOhmiMqXjWvdMYU9UtBe4RkXa4xzP8K2DeycBPEYnKGGNMiVJSUnj77bfZt2+fFfiMiSBr6TPGVHW3AAuA/wVWAYH33V0GfBSJoMKxJG1JiWn6tuxb4XEYY0wkfPTRR6xYsYLRo0fTsGHDSIdTbrKyYMoUmD4d0tOhUSO49lq46SYIeia5MVWGFfqMMVWaqn4PJIhIQ1X9NWj2/cD+CIRljClnNjJn9ZGbm4uI0LJlS7p06VIuz1CrKrKy4LTTYMMG2OtvNNi+He6/H958Ez75pPQFv5iYGDp16pT/SIsrrriCCRMmcNhhh94x76677qJPnz4hH0IO8MQTTxAfH88VV1xRumC9tLQ0EhISOPnkk9m/fz99+vRh+vTpRcYcznZTU1PZsmULQ4YMKVNspiAr9BljokKIAh+qujMCoRhjjCnC/v37ef311+nQoQNJSUmRDqfcTZlSsMCXZ+9e9/mUKXD33aVbd+3atUlNTQVg69atjBgxgl27dnF3KVY4adKkYuePHTu2NCGGdOKJJ5Kamkp2djb9+/dn7ty5XHDBBaXebmpqKikpKVboK2d2T58xxhhTgjfecC9jqr/S33f322+/MWvWLOrUqUNiYmI5xlR1TJ9euMCXZ+/eg4M+lVWTJk146qmnmDZtGqpKTk4ON910E927dycxMZEnn3wyP+19991Hp06d6Ny5M7feeisAycnJvOEzrVtvvZX27duTmJjIn//8ZwAmTpzIAw88ALhC1mmnnUZiYiLnn38+O3bsAKBv377ccsst9OjRg7Zt27J06dJiY46NjaVnz56sX7+etLQ0+vfvT2JiIgMGDGDjxo2Fthtq/fv37+euu+7i1VdfJSkpiVfzhk42ZWYtfcYYY0wJjj460hEYU/UtW7aMNm3a0Ldv32o7aEt6CU9oLWn+oWjdujU5OTls3bqVefPmUb9+fT777DP27dtHr169GDRoEN988w3z5s3j008/JT4+nl9/LdgpJj09nbfeeotvvvkGEWHnzp2FtnPFFVcwdepUzjzzTO666y7uvvtuHnnkEQCys7NZvnw5CxYs4O6772bRokVFxrt7924WL17MpEmTuP766xk1ahSjRo3i2Wef5YYbbmDu3LmFlgm1/kmTJpGSksK0aTZodnmyQp8xxhhTgpkz3d/k5EhGYUzVtHnzZmrVqsVZZ51VbQt7eRo1cvfwFTe/IixcuJAvv/wyv/UuIyODb7/9lkWLFjF69Gji4+MBCg2YU79+feLi4rjqqqsYOnQoQ4cOLTA/IyODnTt3cuaZZwIwatQoLr744vz5ed00TznlFNLS0kLGtmHDBpKSkhARzj33XH73u98xcuRI5syZA8DIkSO5+eabQy4bzvpN+bBCnzHGGFMCK/SVjQ3SUn2tW7eOefPmcf7559OkSZNIh1Phrr3WDdoSqotnXByMG1d+2/ruu++IiYmhSZMmqCpTp07l7LPPLpDmvffeK3YdsbGxLF++nMWLF/PGG28wbdo03n///bBjOOKIIwA3yEx2dnbINHn39JVGOOs35cMKfcYYE0FhPdahwqMwxphDt3LlShYtWsSll15K8+bNIx1OpbjpJjdKZ/BgLnFxcOKJbn552LZtG2PHjmX8+PGICGeffTb//Oc/6d+/P7Vq1WLdunU0a9aMgQMHMmnSJC677LL87p2BrX1ZWVns3r2bIUOG0KtXL1q3bl1gO/Xr16dBgwYsXbqU3r17M3v27PxWv7Lo2bMnr7zyCiNHjuTFF1+kd+/eYS9bt25dMjMzyxyDKcgKfcYYY4wxJmyqCkCjRo1ITk6mUUX1aayC6tRxj2WYMsUN2pL3nL5x48r+nL49e/aQlJSU/8iGkSNHcuONNwIwZswY0tLS6Nq1K6pK48aNmTt3LoMHDyY1NZVu3bpx+OGHM2TIECZPnpy/zszMTM4991z27t2LqvLQQw8V2u6sWbMYO3Ysu3fvpnXr1jz33HOl3wlv6tSpjB49milTptC4ceNDWme/fv249957SUpK4i9/+QvDhw8vczwGJO/Ercp27c2t+kGasO07kBvpEEw5a1w3tnrfxFEK3Y47Th8Y1LZc1hXWA9wnTiyXbZnQ+vZ1f5csiWQUVU80d9u8u9/dn6tqt0jHUdV0S0rQlNQ1Rc7Pzc3l3XffpVGjRvTs2bMSI6tYa9asISEhIdJhGJMv1HdSREqdb1lLnzHGVHHWBdSUt3AKaxP7lpzG1Cz79+/nzTffJCcnh0GDBkU6HGPMIbBCnzHGGFOCBQsqdv3l2WJWXoW1aG7FMxVj+fLl1K5dm3POOYeYmJhIh2OMOQRW6DPGmBqkvC7ka1orkB8NvVQqu/BkhTVT3nbs2MG+ffvo2bMnIlJtH8ugqtV230x0qYjb76zQZ4wx1YBd6Fes6dPd32uvPfiZHXNTE2zZsoWXX36Z/v37c8wxx0Q6nAoTFxdHeno6jRo1soKfiShVJT09nbi4uHJdrxX6jDGmGug7c0mJaZYk9y237YVb4AmnRbCyuzaWZnszn0oGYGv7mYe8rDHRasOGDcyZM4ehQ4dW+0FOmjdvzqZNm9i2bVukQzGGuLi4cn8MihX6jDHGVBjr2mhMtHGtXKpKfHw8w4cPp0WLFhGOqeLVqlWLVq1aRToMYyqMFfqMMTWeiAwGHgVigGdU9d6g+UcAzwOnAOnAcFVNq+w4yyqc1sBwlGeLoTGm9Com71I+/PBDDhw4wIABAyoibGNMBFihzxhTo4lIDPA4MBDYBHwmIvNVdXVAsquAHaraRkQuAe4DauzTYq3waEzkVVTelZG1l9WrV3PZZZdVVOjGmAiwQp8xpqbrAaxX1e8AROQV4Fwg8MLpXGCif/8GME1ERCtieK0apLwKj5Vh7s/nAS5mK6yaKqJC8q7s7BxGjx7NEUccUTFRG2MiIioKffXiDqsRwyiJyNWq+lSk46hwcYdFOoJKU2P+p9GtGfBjwPQm4NSi0qhqtohkAI2A7YGJRORq4Go/ua/frJ9WVUjEFe9ogvYtSlRg3F0A6DcLmPWfitiAHfPKd3KkAyijCsu74uLiojHviubvYrTGHq1xQ/TGXup8KyoKfTXI1YAVEKoX+5/WIL6A/xSAiKSoarcIh1Qq0Rp7tMYN0Rt7tMYNLvZIx1BVVIe8K1rjhuiNPVrjhuiNvSz5Vs1pcjHGmNA2A8cHTDf3n4VMIyKxQH3coAjGGBMplncZY8JmhT5jTE33GXCSiLQSkcOBS4D5QWnmA6P8+4uA9+1+PmNMhFneZYwJm3XvrFqsG2D1Y//TKs7f5zIeeA837Pmzqvq1iEwCUlR1PjADmC0i64FfcRdXJYnm/320xh6tcUP0xh6tcUN0x255V2HRGjdEb+zRGjdEb+yljluswscYY4wxxhhjqi/r3mmMMcYYY4wx1ZgV+owxxhhjjDGmGrNCXxUhIoNFZK2IrBeRWyMdjykbEXlWRLaKSDQ+68gcgpLOXRE5QkRe9fM/FZGWEQgzpDBiv1FEVovIlyKyWEROiEScwcLNL0XkQhFREakyw3KHE7uI/I8/7l+LyEuVHWMoYXxXWojIByLyhf++DIlEnMFKyovFeczv15ci0rWyY4wEy7cqn+Vblc/yrSCqaq8Iv3A3YG8AWgOHAyuB9pGOy15l+p/2AboCqyIdi70q9P9c4rkLXAs84d9fArwa6bgPIfZ+QLx/P64qxB5ufgnUBT4EPgG6RTruQzjmJwFfAA38dJMoifspYJx/3x5Ii3TcPpZi82JgCPB/gACnAZ9GOuYq8v+0fKuS4/bpLN+q3LhrVL5lLX1VQw9gvap+p6r7gVeAcyMckykDVf0QN1Kaqd7COXfPBWb5928AA0REKjHGopQYu6p+oKq7/eQnuOeARVq4+eU9wH3A3soMrgThxP4H4HFV3QGgqlsrOcZQwolbgXr+fX1gSyXGV6Qw8uJzgefV+QQ4SkSOrZzoIsbyrcpn+Vbls3wriBX6qoZmwI8B05v8Z8aYqi2cczc/japmAxlAo0qJrniHmu9chatZjLQS4/ZdXY5X1XcrM7AwhHPM2wJtReRjEflERAZXWnRFCyfuicDlIrIJWABcXzmhlVlN/P21fKvyWb5V+SzfCmLP6TPGGFMsEbkc6AacGelYSiIihwEPAckRDqW0YnFdpfriWig+FJFOqrozkkGF4VJgpqo+KCKn454N11FVcyMdmKmZLN+qVJZvRQFr6asaNgPHB0w3958ZY6q2cM7d/DQiEovrQpJeKdEVL6x8R0TOAm4HhqnqvkqKrTglxV0X6AgsEZE03P0O86vIoAjhHPNNwHxVPaCq3wPrcBdTkRRO3FcBrwGo6n+BOODoSomubGri76/lW5XP8q3KZ/lWECv0VQ2fASeJSCsRORx30/T8CMdkjClZOOfufGCUf38R8L76O7EjrMTYRaQL8CTuwqkq3KMBJcStqhmqerSqtlTVlrh7eoapakpkwi0gnO/LXFxtOSJyNK7b1HeVGGMo4cS9ERgAICIJuIunbZUaZenMB67wo+GdBmSo6k+RDqqCWb5V+SzfqnyWbwWx7p1VgKpmi8h44D3caEPPqurXEQ7LlIGIvIzLAI/2fcX/qqozIhuVKW9FnbsiMglIUdX5wAxcl5H1uBuzL4lcxAeFGfsUoA7wuh/DYaOqDotY0IQdd5UUZuzvAYNEZDWQA9ykqhFtYQkz7j8BT4vIBNzgCMlVoZAQKi8GagGo6hO4+3iGAOuB3cDoyERaeSzfqnyWb1U+y7dCrLcK7JsxxhhjjDHGmApi3TuNMcYYY4wxphqzQp8xxhhjjDHGVGNW6DPGGGOMMcaYaswKfcYYY4wxxhhTjVmhzxhjjDHGGGOqMSv0GUTkDBHRgOknRGRaJcewSEQmVtC6+4pIdkWs25hoFM45Hol8oCzKep6LSEsRURFpXkya/xORmwOmVUTO8O97i8jO0m6/mG3Gici3InJyea+7hO029/vX0k/fJiJvV3IMz4jITP/+CBFZLyLtKjMGY/KIyBIRuSPScRwKEflaRIaXNY2pHqzQV8X5TGafiGSJSIaIfCEiF1bkNlV1rKqOP4T4KiwTFJHDRWSriFwZYl4dEdklIhdV1PaNiaSKOv+Dz3ERSRORy4tLU978Nvf6fdshIh+LSN+K2l55UNXfqer9RcxbqqpH5U2LyEQRWVQOm/1f4L+qurYc1lVqqjpZVc8JJ62IzBSRZ8p5+/twz1+bUp7rNTVPUL6a9yrX72spYlIR2e1j2S4iC0Wkc1nXq6odVPVVv42QFVuBacpbwDZ/8/u2VUTeEpFWh7CO8spLazwr9EWHe1S1DtAIeBl4VUTaBicSkVqVHlkFU9X9wEzgDyFmX4p7KOW8yozJmEoW1vkfpcb4fTsO+Bx4W0TqByeqjnlbOEQkBhgPPF2WdYhIdfmtfxnoLyJtIh2IiXr3qGqdgNeYSAcEDPL54YlABlCpLesV7GS/bx2Ao4DnIhtOzVRdfghqBFXNBqYDMUCnvO5MIjJSRL4DfgUQkRYi8oaI/CwiP4nIUyJSN289InKSr+nKFJGVQLfA7QTX0opIYxGZISIbfcvaChE52Xf96g3c6Wtw1gYs8wcRWRXQOjEoYJ6IyF9EZJOI/CoiDwNSzK4/BZwqIp2CPr8al3HUEpE5fn/z4htY1MpC1UIHt3SI66r1kY9vg4j8SUTEz2sgIq+LSLrfv69FpHcx8RtTZsHnP4CIjBORtf57+Eng91BEuvjvcIb/Hi8TkQZ+Xv45IK7LXgvgGX8eLwyRZoqIzA2Mx+c/mSJypJ/uKCLvicg2n1f8I9zCmqruwZ3ndYA2IpIsrivfTSKyCUj12zhTRD71+/SNiFwTvC4RGSUiP/h9nikidQLmTRaR7/x+bhCRP4YIZ7CIrPPbmCciTQKWL7JngwR0LxXXVeo2oK8cbEk4UUQ2i8j5Qcs9LyIzijg03YAGwLKA9HnH5hZx+ftWEXkw71jLwZr1q0RkNa5irImINBKXj//o/0eviUjTgPUeIyLz/X6vAwYHxVmgtl1cT4sH/PHMFJHVPt+8GbgMGBWw7zF+mfNE5HMR2Skia0TksqBtXOn/L7tEZDYQFzhfVXcBnwHDijhexpSaiFwiIiv99+8nEXkyL38LkfYIcddWW336b0Xk4oD5RV5DlERVM4BZwPH+vI0XkUf9ubtdROaKSIuguNf48/AXEZkVMC/w2mal/7vWn5d3BqcRkc+C80V/7n8QMF3seVzCvm0D3iDgulNEOovIf/y+7RDXjf5EPy9UXtrazyv1Ma6prNAXRUTkcOA64AAHT94YYAjQBWgqInHA+8BqoBXQHmgOPOrXEQu8A3wNNAEuAsYWs83DgPm4mpnu/m8ykOm7fi3lYI3ZyX6ZPwC34H74GwC3A3PkYO3s5cAE4FzgGGA70KeoGFR1PfABAa19IpIEnIKrAT8MmAOcxMHWkDdFpHFR6yyOiLQHFuC6ETUGfo+rbR/pk9wExAMn+ONxPrCpNNsyJlzB57+IXArcA1yB+94/DfxLRE7wizwOLAQaAk2BG4H9wev1XfY24lvdVHVQcBpc5cqQoHNqNPCaqv4mrmD0H9x52Aw4HRgI/CXMfTsSuAZXu73Of9wS1wJ4EtBdXHegfwH/9PubDPwj8EILlx+eAyQCCUBb4KGA+auBM4C6uPzkHyJydlA4V+Dyo+OBXOCFcPYhkO8qNRlYEtCSsAGYAeS3KIhr1byIolvyugLrVDUn6PMTcAX11rhjfQ4uXwo0AuiP29dtwFxAgY5++UzgpYD0LwI5fr19cMe3ODOAU4EBQD1cQewn3/31RWBWwL7niKuImwH8EfedHAVME5E+/lj0xn1nx/r5/wZC3Wf0lT8uxpS3DNx5cxSuQrs3UNTtK6Nw10QJqloPd659DWFdQxRLXOVcMvC9qqYDDwOn+dcJuGumt8W14scDs4HrVLUuLk8oqqtqXnfRk/15eU+INM8RcO77QtQo4Fk/Xex5HMa+HYM7rwO7qyswEffb0RLIwue7ReSl35X1GNdYqmqvKvwClgB7gJ3AVlyN7zl+Xl/cydIiIP1FwIagdZwC7MNdEPXy7+MD5v/BfRXyp2cCz/j3PXAXmfWLie+OoM9WAVcEffZ2Xjrcj/k9AfMOA34EJhZzHP4H15IZ56f/CbxXTPrtwJCA45Qdav8CPksDLvfvpwHPBs3/E7DIv58IfOKP62GR/o7Yq/q+Sjj/FwJ/D0r/X+AvAcs+A7QMsd4C50Dg97+YNJ8CE/z7usBvQC8//Wfg/aDlLwTWF7NvaX4dO4FfgMUB60v2+31EQPrbgI+D1vGPvHyAg/nhiQHzzwL2FnWe4mqc7/fvW/rlBwTMb+M/Oy7gmN4RMF+BMwK2H5jPTMzLMwI+a4EreDfz0+OAr4o5RrfhLnYCP0umcB4+Blc4DNyPPgHzu+Fa/AKPZyOfrjnuYiv42A30n7UM3h9chaECHYqIu8B3x3/2DnBX0GdTOfhb8zQwO2j+x8DMoM/+DiyI9Llpr+h9UTBfzXudFiLdeGB50HJ51zHJwLe4gmFs0HLFXkMUEZPiKmJ2Altwle0dcNdHe4GBAWnr+HzkdFwF9G7gWqBhiPWmcfDaJi9vaF5MmgZ+e138dH9cYbi2ny72PA6x/bxtZvj9U1zFTbtijkVHny7eT+fnPWU5xvZSa+mLEn9X1aNUtYmq9lTVwH7eubgCU55WQAvf7L5T3Ghyi3En0DG4H/itqro7YJnvi9l2S58+4xDibQU8HhRDP9yFBT6GtLzEqpoL/FDCOt/CFT4v9q0CI3DdwRCR2iIyzXcz2uW31wBX+1MarYBLg+L/K3Csnz8Fd0xnAdtEZJYEdJMyppwVdf4fT+Fzd4P/HFxL3GHARyLyvYjc41v6SyuwBvh/gE2q+rGfbgX0CjpnnsXlOcW5xu9bU1UdELA+cK1G+wKmS9rfPIF5SRpwBHA0gIjcICJf+S5EO3EtZMH5RFqI90WO6HkoVHUjrtJrtP9oDMXfr7cD14oWLDgPTwsRY1rA+1a44/BLwP9nA+7irkXAsoHHrqTfBTjYKhuOVsAtQd+RZFxrLgT9LhQTQz38rQzGlEFevpr3+kREBorIUnHdn3cB91H0dcQLuEq1h4F0cbeY5PVmKukaoii/87Ecp6rDVPVrv/0jCDgXVDULVwl4vM8HhuC6Y2/w3S5HlOaA+HXvwPUKyMujRgOvqOuCn7dvxZ3HRemgriWyO66FMH8gF3Fd3+eI6/6+C1fZA8Vfw5X2GNdoVuiLfqq+isP7AVfje1TQK05VNwObcfd3xAcs07KY9af59KEuPMAVOoP9AFwZtP06qjrOz98cuE3ffeCEEOsJ3MkDHBzQ5RJczdZ8P/tGXHekAbgWyaNwF0tF9e3OBPL76fsL4SYB83/A1SAFxl9PVTv4WH5T1dtVtSOuJq4ZNqKcqXw/Uvjcbe0/R1W/V9UrVbU5ruvdGFzXxVBCncfBXgHaikhX3I/8cwHzfsDVsAaeM/XV3bhfWsExFbu/AQLzkpa4VrHtItILdxF3DXC0zyfepnA+0TLE+9J03y7qmD4JjBaRLrju97OLWccXuGMeE/R5qDw8OMbA7f+Aa1VtGPQ/qq2qy3B5MhQ+dkVJ839PKmJ+Ub8LE4O2X1dVh/j5BX4XiomhI+64GFNufPf5ubh8roW6Lpu3UMR1hKpmq+p9qtoNd97sxneBpIRriEO0DZeHtQyItQ7umiUvr1+iqsNwlVt/A17IuycuSDj5PLi8fYSIHA1cQOG8vrjzuFiqmoLrMvt0QB72BO66LNEf9155u1pM3OV5jGsMK/RVP+8Ah4t7plJdcZrJwcEDPsGdLPf5FrITcYWmoqQAK3CDPDQRkcNEJFFE8mp1fsZ1gQr0MDBRRJL89muLexZg3vOVZgNXi0hXcYMP3ErJLQLgWvbOwN0j+KwvCIKr+d0HpPt9vwvXJ78onwMDRKSViByB6y4UOODEdOASETlHRGqJSKyItBeRMwH85wn+QiwLV1sefM+NMRVtJnCNiPTw39HRQBL+Pi1xA5rknac7gWyK/p7+TNEX8ACo6k5ci/vfcPeWzAqY/TzQTdxAHHE+n2gtIoNDrKq0XgZOEZEr/P72wBXgggdB+YeI1BN3n+FEXJfBXFw+kYO7iFIR+T3wuxDbuVNEmvqKrvtwhdktpYj3Z1yvi8ODPn8XV3M/A3jT16wX5TPc/+70oM8P42Ae3hrXvXYWRUvB3Qf+mIg0gvwBui4BUNVNuK5r9/tj1xS4q6iVqepWXNfY6eIGjhERaRPQ0vEz0FoKjhr6CDBB3OALMeIex3OKiOQN6DAbuEhEBvj/7+W4ewbziRuQrAcHK/yMKS+H487LHaq6R9w9Y0U+skZE+vvvby1cV9HfOJi/FnsNcSh83vU8cI+IHOcLSg8C3wDLfV51oYjUV3fv706/aKi8fhuuAFVsXo/rjbDHbzdNVT8JmPcIxZ/H4XgeV0i+wU/Xw3f19wXNSUHpQ+Wl5XaMaxIr9FUzvqm/P64G+RtcP+rFuItB1I0AOAw30MFW3MALTxWzvlxcF6g9uBH0duJqs/Jq8B/GXeztFJGv/TJPA/fjaod24AaJuJODBavncX3A38bdy9ME+DCMfdvg96UlBbtEPcTBfvAbcJlJWjGrehF30bDCp9/IwZpuVHUVMBR3o/JPuOM0k4NdDU70se/y29mDqxE0ptKo6kvA3bhuRum4+8OGqGpeF73+wOci8hvuXr+XKLpV6W/A5eJHTitms8/hCkrvqepPAbH8jOvCfR7unNiBKyC2LtXOhaCq3+O6MY3H7e9s4E5VfS0gWQ6uUPUVbqCA7zhYqfUeLu9Zjrvn9yIfY7AXcANU/Yi7ECztwACv+3X87PPHVn4/cnAFvi6U8CgGn3YaAYO/eD/gWva+x91r+S9cnlvUenJxA2cJ7juRiasA7BuQbATuovdH3P4/X8L+XYn7TfgPrpZ+Hgcr757B9aZI9/seo6oLcT01puCO/0+43486Psb/ANf7ZX/FdVcLfnbYpcAHqvptCbEZc0h8l8lxuIqPLNygQi8Vs0hTXB60A/ddPgE3ong41xCHagKu4uYz3PXKscAwnz8chhvgK82f148Do1Q1LcQ+7sFdi73sz8vbQ20soKD5O4IerVDSeRwOH/ckXDfRBn7/euOuqZbiGi8CFcpLK+AY1whSsGegMcYYYyqSiCTjBtw5OYy0tYEvgaGqutYve4eq1qhn1fleGatwF7trIh2PMcZEm7Lc1G+MMcaYQ+C7KP4v8Fg46X3tfEndsao9dYP61PjjYIwxpWXdO40xxphKIO6hx7/gumcW2a3eGGOMKW/WvdMYY4wxxhhjqjFr6TPGGGOMMcaYaswKfcYYY4wxxhhTjVmhzxhjjDHGGGOqMSv0GWOMMcYYY0w1ZoU+Y4wxxhhjjKnG/h/xDWsyJYxXBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.907 | Recall: 0.663 | F1 Score: 0.766 | AUC: 0.948\n"
     ]
    }
   ],
   "source": [
    "### RFE CV performances ###\n",
    "\n",
    "binary_performances(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82aeb93e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T21:38:40.729694Z",
     "iopub.status.busy": "2022-01-10T21:38:40.714130Z",
     "iopub.status.idle": "2022-01-10T21:38:41.054995Z",
     "shell.execute_reply": "2022-01-10T21:38:41.055598Z",
     "shell.execute_reply.started": "2022-01-05T13:48:15.643458Z"
    },
    "papermill": {
     "duration": 0.533809,
     "end_time": "2022-01-10T21:38:41.055776",
     "exception": false,
     "start_time": "2022-01-10T21:38:40.521967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### SELECTED FEATURES ###\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(\n",
    "    range(n_feat), \n",
    "    np.sum(list(rfe_results.values()), axis=0), \n",
    "    label='RFE', c='black', linestyle='--'\n",
    ")\n",
    "plt.plot(\n",
    "    range(n_feat), \n",
    "    np.sum(list(rfa_results.values()), axis=0), \n",
    "    label='RFA', c='black'\n",
    ")\n",
    "plt.xlim(0, n_feat-1)\n",
    "plt.ylim(-0.5,len(rfe_results)+0.5)\n",
    "plt.axvspan(informartive_feat[0], informartive_feat[-1], alpha=0.3, color='green', label='informative')\n",
    "plt.axvspan(redundant_feat[0]-1, redundant_feat[-1], alpha=0.3, color='orange', label='redundant')\n",
    "plt.axvspan(noise_feat[0]-1, noise_feat[-1], alpha=0.3, color='red', label='noise')\n",
    "plt.xticks(range(n_feat), X.columns, rotation=90)\n",
    "plt.title('How many time a feature is selected')\n",
    "plt.ylabel('CV folds')\n",
    "plt.legend(); plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 2422.737059,
   "end_time": "2022-01-10T21:38:42.351245",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-01-10T20:58:19.614186",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
